ExPLoRA: Parameter-Efficient Extended Pre-Training to Adapt Vision Transformers under Domain Shifts

Samar Khanna, Medhanie Irgau, David B. Lobell, Stefano Ermon·June 16, 2024

Summary

ExPLoRA is a parameter-efficient technique for adapting vision transformers (ViTs) to new domains, particularly in satellite imagery, by extending pre-training with low-rank adaptation (LoRA). It unfreezes only a few pre-trained blocks and normalization layers, using objectives like DinoV2. This approach outperforms fully pre-trained models with significantly fewer parameters, achieving up to a 7% improvement in linear probing accuracy on satellite tasks. ExPLoRA compares favorably to other baselines, including PEFT and deeper unfreezing, and has been extended to D-ExPLoRA, which further reduces parameter count while maintaining performance. The method is versatile, demonstrating strong results on satellite, wildlife, and medical image datasets, and contributes to more efficient and cost-effective use of foundation models in various domains.

Key findings

4

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of adapting pre-trained Vision Transformers (ViT) from natural images to other visual domains like satellite imagery or medical data in a parameter-efficient manner . This problem is not entirely new, but the paper introduces a novel pre-training strategy called ExPLoRA to tackle this challenge effectively .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the hypothesis related to the adaptation of pre-trained weights to fine-tuned weights in the context of different domains. The traditional hypothesis, known as LoRA, suggests that the update when adapting pre-trained weights to fine-tuned weights resides in a low-rank subspace, which works well when the pre-training and fine-tuning distributions are similar. However, the paper empirically finds that this hypothesis may not hold when adapting to significantly different domains where there is little to no overlap between the distributions . The goal of the paper is to explore whether it is possible to leverage the semantic information encoded in pre-trained weights from one domain to efficiently learn weights for a new domain, without the need for full-rank pre-training from scratch for each new domain of interest .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "ExPLoRA: Parameter-Efficient Extended Pre-Training to Adapt Vision Transformers under Domain Shifts" introduces several novel ideas, methods, and models in the field of vision transformers and domain adaptation . Here are the key contributions of the paper:

  1. ExPLoRA Method: The paper introduces the ExPLoRA method, which is a parameter-efficient approach that extends unsupervised pre-training on target domains. This method achieves state-of-the-art supervised-learning performance using only a fraction of the original ViT weights .

  2. Case Study on Satellite Imagery: The research conducts a comprehensive case study on satellite imagery, demonstrating improvements in linear probing top-1 accuracy and outperforming existing techniques on datasets like fMoW. The ExPLoRA method showcases superior performance in adapting to different domains, such as wildlife and medical imagery .

  3. Transfer Learning and Generalization: The paper highlights the effectiveness of ExPLoRA in transferring knowledge from foundation models to additional visual domains like satellite imagery and medical data. It challenges the traditional paradigm of expensive pre-training from scratch for each new visual domain by offering a parameter-efficient and effective solution for knowledge transfer .

  4. Broader Impact and Future Directions: The study discusses the broader impact of techniques like PEFT in enabling researchers with limited computational resources to leverage foundation models for various domains. It emphasizes the importance of accelerating the deployment of machine learning in critical domains such as sustainability and medicine. The paper also suggests avenues for further research, including investigating the combination of ExPLoRA with other parameter-efficient techniques and evaluating its applicability in natural language domains . The paper "ExPLoRA: Parameter-Efficient Extended Pre-Training to Adapt Vision Transformers under Domain Shifts" introduces several key characteristics and advantages compared to previous methods in the field of vision transformers and domain adaptation .

  5. Parameter-Efficient Approach: ExPLoRA presents a parameter-efficient method that extends unsupervised pre-training on target domains, achieving state-of-the-art supervised-learning performance using only a fraction of the original ViT weights. This approach allows for efficient adaptation to downstream tasks without the need for full-rank pre-training from scratch, showcasing superior performance in linear probing accuracy and outperforming existing techniques .

  6. Generalization to Different Domains: The ExPLoRA method demonstrates generalization to various domains, such as wildlife imagery and medical images, showcasing improvements in linear probing accuracy and outperforming prior methods that require full pre-training from scratch. This ability to adapt to different domains effectively bridges the gap between natural images and new visual domains, offering a more efficient and effective transfer learning solution .

  7. Efficient Knowledge Transfer: By leveraging pre-trained weights from large, natural-image datasets like DinoV2 or MAE, ExPLoRA continues unsupervised pre-training on new domains while selectively unfreezing specific ViT blocks and utilizing techniques like LoRA for parameter-efficient adaptation. This approach results in significant improvements in linear probing accuracy and outperforms fully pre-trained state-of-the-art methods, showcasing the effectiveness of knowledge transfer from pre-training on natural images to new domains .

  8. State-of-the-Art Results: The experiments conducted in the paper demonstrate state-of-the-art results on satellite imagery, surpassing fully pre-trained and fine-tuned ViTs. ExPLoRA achieves up to a 7% improvement in linear probing top-1 accuracy on downstream tasks while using a significantly lower number of parameters compared to traditional approaches, highlighting its efficiency and effectiveness in adapting vision transformers under domain shifts .

In summary, ExPLoRA's characteristics include parameter efficiency, generalization to different domains, efficient knowledge transfer, and the ability to achieve state-of-the-art results in adapting vision transformers to new visual domains, setting it apart from previous methods in the field .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field, and notable researchers have contributed to this area. Some noteworthy researchers include Weiyang Liu, Zeju Qiu, Yao Feng, Yuliang Xiu, Yuxuan Xue, Longhui Yu, Haiwen Feng, Zhen Liu, Juyeon Heo, Songyou Peng, and many others . Additionally, researchers like Peter Bandi, Oscar Geessink, Quirine Manson, Marcory Van Dijk, Maschenka Balkenhol, and others have also made significant contributions .

The key to the solution mentioned in the paper involves leveraging the rich semantic information encoded in pre-trained weights (WDP) and learning new domain weights (WDF) in a parameter-efficient manner. This is achieved by factorizing the fine-tuned weights for a new domain as follows: WdF ≈ WDP + ∆DF, where WDF represents unsupervised pre-trained weights learned from the new data distribution .


How were the experiments in the paper designed?

The experiments in the paper were designed to introduce ExPLoRA, a method for enhancing transfer learning of pre-trained vision transformers under domain shifts. The experiments involved initializing a ViT with pre-trained weights from large, natural-image datasets like DinoV2 or MAE, then continuing unsupervised pre-training on a new domain. In this extended pre-training phase, only 1-2 pre-trained ViT blocks and all normalization layers were unfrozen, while all other layers were tuned with LoRA. Finally, the resulting model was fine-tuned solely with LoRA on the new domain for supervised learning .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the EuroSAT dataset, which contains 27,000 13-band satellite images of 10 classes sourced from Sentinel-2 . The code for the dataset is open source, and the license for EuroSAT is provided at https://creativecommons.org/licenses/by/4.0/ .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The research introduces ExPLoRA, a novel pre-training strategy aimed at adapting pre-trained Vision Transformers for various visual domains like satellite imagery and medical data . The study conducts a comprehensive analysis on RGB, temporal, and multi-spectral satellite images, demonstrating either matching or outperforming existing methods that utilize full-rank pre-training from scratch . This indicates that ExPLoRA is effective in transferring knowledge from foundation models to different domains in a parameter-efficient manner, showcasing state-of-the-art supervised-learning performance with a fraction of the original ViT weights .

Furthermore, the results of the experiments, particularly in the context of linear probing accuracy, show significant improvements over prior state-of-the-art methods . The study reports an increase of over 7.3% in top-1 average accuracy compared to previous techniques, highlighting the robust unsupervised representations learned by ExPLoRA for the target domain without the need for expensive from-scratch pre-training . This improvement in performance, especially in linear probing, indicates the effectiveness of ExPLoRA in distilling knowledge from pre-trained models for different visual domains, showcasing successful transfer learning .

Moreover, the paper's broader impact section emphasizes the importance of techniques like PEFT, which can reduce the computational resources required for leveraging foundation models in various domains . By enabling researchers with limited computational resources to customize models for their specific needs, ExPLoRA accelerates the deployment and use of machine learning in critical domains such as sustainability and medicine . This aspect further supports the scientific hypotheses by demonstrating the practical implications and benefits of the proposed method in real-world applications .


What are the contributions of this paper?

The contributions of the paper "ExPLoRA: Parameter-Efficient Extended Pre-Training to Adapt Vision Transformers under Domain Shifts" include:

  1. Introducing ExPLoRA, a novel parameter-efficient method that extends unsupervised pre-training on target domains, achieving state-of-the-art supervised-learning performance using a fraction of the original ViT weights .
  2. Conducting a comprehensive case study on satellite imagery, showcasing improvements in linear probing top-1 accuracy and outperforming existing techniques on datasets like fMoW. The study also demonstrates generalization to other domains such as wildlife and medical imagery .

What work can be continued in depth?

Further research can delve into various aspects related to ExPLoRA that warrant deeper exploration. One area of interest is understanding the effectiveness of unfreezing a small number of blocks in combination with PEFT techniques like LoRA, which has shown promising results . Additionally, investigating the potential of other parameter-efficient methods to enhance the pre-training process with ExPLoRA could provide valuable insights . Furthermore, there is a need to evaluate the applicability of ExPLoRA in natural language domains and explore the possibility of eliminating the need to unfreeze a transformer block entirely .

Tables

1

Introduction
Background
[Overview of Vision Transformers (ViTs) in computer vision]
[Challenges in adapting ViTs to satellite imagery]
Objective
To develop a lightweight adaptation method for ViTs
Improve performance with minimal parameter increase
Enable efficient use of foundation models across domains
Method
Data Collection
[Satellite imagery datasets and data preprocessing]
[Data augmentation techniques for domain adaptation]
Low-Rank Adaptation (LoRA)
Unfreezing Pre-Trained Blocks
Selective unfreezing strategy
Importance of specific layers for adaptation
Normalization Layers
Handling domain shifts with layer-wise adaptation
DinoV2 Objective
Incorporating self-supervised learning for adaptation
ExPLoRA Algorithm
Training procedure and optimization
Hyperparameter selection for efficiency
Performance Comparison
Baselines
PEFT (Pre-Training with Explicit Finetuning)
Deeper Unfreezing
Fully Pre-Trained Models
Accuracy Improvements
Linear probing results on satellite tasks
Percentage improvements over baselines
D-ExPLoRA Extension
Reducing parameter count without compromising performance
Advantages and trade-offs
Applications
Satellite Imagery
Case studies and real-world applications
Domain-specific improvements
Wildlife and Medical Images
Transfer learning to diverse domains
Adaptation effectiveness across modalities
Efficiency and Cost Benefits
Foundation model cost reduction
Environmental and resource implications
Conclusion
Summary of ExPLoRA's contributions
Future directions and potential improvements
Basic info
papers
computer vision and pattern recognition
artificial intelligence
Advanced features
Insights
How does ExPLoRA compare to fully pre-trained models in terms of parameter efficiency?
What is ExPLoRA primarily designed for?
How does ExPLoRA improve the performance of vision transformers in new domains like satellite imagery?
What are some extensions of ExPLoRA mentioned in the user input, and how do they contribute to the method's effectiveness?

ExPLoRA: Parameter-Efficient Extended Pre-Training to Adapt Vision Transformers under Domain Shifts

Samar Khanna, Medhanie Irgau, David B. Lobell, Stefano Ermon·June 16, 2024

Summary

ExPLoRA is a parameter-efficient technique for adapting vision transformers (ViTs) to new domains, particularly in satellite imagery, by extending pre-training with low-rank adaptation (LoRA). It unfreezes only a few pre-trained blocks and normalization layers, using objectives like DinoV2. This approach outperforms fully pre-trained models with significantly fewer parameters, achieving up to a 7% improvement in linear probing accuracy on satellite tasks. ExPLoRA compares favorably to other baselines, including PEFT and deeper unfreezing, and has been extended to D-ExPLoRA, which further reduces parameter count while maintaining performance. The method is versatile, demonstrating strong results on satellite, wildlife, and medical image datasets, and contributes to more efficient and cost-effective use of foundation models in various domains.
Mind map
Percentage improvements over baselines
Linear probing results on satellite tasks
Fully Pre-Trained Models
Deeper Unfreezing
PEFT (Pre-Training with Explicit Finetuning)
Incorporating self-supervised learning for adaptation
Handling domain shifts with layer-wise adaptation
Importance of specific layers for adaptation
Selective unfreezing strategy
Environmental and resource implications
Foundation model cost reduction
Adaptation effectiveness across modalities
Transfer learning to diverse domains
Domain-specific improvements
Case studies and real-world applications
Advantages and trade-offs
Reducing parameter count without compromising performance
Accuracy Improvements
Baselines
Hyperparameter selection for efficiency
Training procedure and optimization
DinoV2 Objective
Normalization Layers
Unfreezing Pre-Trained Blocks
[Data augmentation techniques for domain adaptation]
[Satellite imagery datasets and data preprocessing]
Enable efficient use of foundation models across domains
Improve performance with minimal parameter increase
To develop a lightweight adaptation method for ViTs
[Challenges in adapting ViTs to satellite imagery]
[Overview of Vision Transformers (ViTs) in computer vision]
Future directions and potential improvements
Summary of ExPLoRA's contributions
Efficiency and Cost Benefits
Wildlife and Medical Images
Satellite Imagery
D-ExPLoRA Extension
Performance Comparison
ExPLoRA Algorithm
Low-Rank Adaptation (LoRA)
Data Collection
Objective
Background
Conclusion
Applications
Method
Introduction
Outline
Introduction
Background
[Overview of Vision Transformers (ViTs) in computer vision]
[Challenges in adapting ViTs to satellite imagery]
Objective
To develop a lightweight adaptation method for ViTs
Improve performance with minimal parameter increase
Enable efficient use of foundation models across domains
Method
Data Collection
[Satellite imagery datasets and data preprocessing]
[Data augmentation techniques for domain adaptation]
Low-Rank Adaptation (LoRA)
Unfreezing Pre-Trained Blocks
Selective unfreezing strategy
Importance of specific layers for adaptation
Normalization Layers
Handling domain shifts with layer-wise adaptation
DinoV2 Objective
Incorporating self-supervised learning for adaptation
ExPLoRA Algorithm
Training procedure and optimization
Hyperparameter selection for efficiency
Performance Comparison
Baselines
PEFT (Pre-Training with Explicit Finetuning)
Deeper Unfreezing
Fully Pre-Trained Models
Accuracy Improvements
Linear probing results on satellite tasks
Percentage improvements over baselines
D-ExPLoRA Extension
Reducing parameter count without compromising performance
Advantages and trade-offs
Applications
Satellite Imagery
Case studies and real-world applications
Domain-specific improvements
Wildlife and Medical Images
Transfer learning to diverse domains
Adaptation effectiveness across modalities
Efficiency and Cost Benefits
Foundation model cost reduction
Environmental and resource implications
Conclusion
Summary of ExPLoRA's contributions
Future directions and potential improvements
Key findings
4

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of adapting pre-trained Vision Transformers (ViT) from natural images to other visual domains like satellite imagery or medical data in a parameter-efficient manner . This problem is not entirely new, but the paper introduces a novel pre-training strategy called ExPLoRA to tackle this challenge effectively .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the hypothesis related to the adaptation of pre-trained weights to fine-tuned weights in the context of different domains. The traditional hypothesis, known as LoRA, suggests that the update when adapting pre-trained weights to fine-tuned weights resides in a low-rank subspace, which works well when the pre-training and fine-tuning distributions are similar. However, the paper empirically finds that this hypothesis may not hold when adapting to significantly different domains where there is little to no overlap between the distributions . The goal of the paper is to explore whether it is possible to leverage the semantic information encoded in pre-trained weights from one domain to efficiently learn weights for a new domain, without the need for full-rank pre-training from scratch for each new domain of interest .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "ExPLoRA: Parameter-Efficient Extended Pre-Training to Adapt Vision Transformers under Domain Shifts" introduces several novel ideas, methods, and models in the field of vision transformers and domain adaptation . Here are the key contributions of the paper:

  1. ExPLoRA Method: The paper introduces the ExPLoRA method, which is a parameter-efficient approach that extends unsupervised pre-training on target domains. This method achieves state-of-the-art supervised-learning performance using only a fraction of the original ViT weights .

  2. Case Study on Satellite Imagery: The research conducts a comprehensive case study on satellite imagery, demonstrating improvements in linear probing top-1 accuracy and outperforming existing techniques on datasets like fMoW. The ExPLoRA method showcases superior performance in adapting to different domains, such as wildlife and medical imagery .

  3. Transfer Learning and Generalization: The paper highlights the effectiveness of ExPLoRA in transferring knowledge from foundation models to additional visual domains like satellite imagery and medical data. It challenges the traditional paradigm of expensive pre-training from scratch for each new visual domain by offering a parameter-efficient and effective solution for knowledge transfer .

  4. Broader Impact and Future Directions: The study discusses the broader impact of techniques like PEFT in enabling researchers with limited computational resources to leverage foundation models for various domains. It emphasizes the importance of accelerating the deployment of machine learning in critical domains such as sustainability and medicine. The paper also suggests avenues for further research, including investigating the combination of ExPLoRA with other parameter-efficient techniques and evaluating its applicability in natural language domains . The paper "ExPLoRA: Parameter-Efficient Extended Pre-Training to Adapt Vision Transformers under Domain Shifts" introduces several key characteristics and advantages compared to previous methods in the field of vision transformers and domain adaptation .

  5. Parameter-Efficient Approach: ExPLoRA presents a parameter-efficient method that extends unsupervised pre-training on target domains, achieving state-of-the-art supervised-learning performance using only a fraction of the original ViT weights. This approach allows for efficient adaptation to downstream tasks without the need for full-rank pre-training from scratch, showcasing superior performance in linear probing accuracy and outperforming existing techniques .

  6. Generalization to Different Domains: The ExPLoRA method demonstrates generalization to various domains, such as wildlife imagery and medical images, showcasing improvements in linear probing accuracy and outperforming prior methods that require full pre-training from scratch. This ability to adapt to different domains effectively bridges the gap between natural images and new visual domains, offering a more efficient and effective transfer learning solution .

  7. Efficient Knowledge Transfer: By leveraging pre-trained weights from large, natural-image datasets like DinoV2 or MAE, ExPLoRA continues unsupervised pre-training on new domains while selectively unfreezing specific ViT blocks and utilizing techniques like LoRA for parameter-efficient adaptation. This approach results in significant improvements in linear probing accuracy and outperforms fully pre-trained state-of-the-art methods, showcasing the effectiveness of knowledge transfer from pre-training on natural images to new domains .

  8. State-of-the-Art Results: The experiments conducted in the paper demonstrate state-of-the-art results on satellite imagery, surpassing fully pre-trained and fine-tuned ViTs. ExPLoRA achieves up to a 7% improvement in linear probing top-1 accuracy on downstream tasks while using a significantly lower number of parameters compared to traditional approaches, highlighting its efficiency and effectiveness in adapting vision transformers under domain shifts .

In summary, ExPLoRA's characteristics include parameter efficiency, generalization to different domains, efficient knowledge transfer, and the ability to achieve state-of-the-art results in adapting vision transformers to new visual domains, setting it apart from previous methods in the field .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field, and notable researchers have contributed to this area. Some noteworthy researchers include Weiyang Liu, Zeju Qiu, Yao Feng, Yuliang Xiu, Yuxuan Xue, Longhui Yu, Haiwen Feng, Zhen Liu, Juyeon Heo, Songyou Peng, and many others . Additionally, researchers like Peter Bandi, Oscar Geessink, Quirine Manson, Marcory Van Dijk, Maschenka Balkenhol, and others have also made significant contributions .

The key to the solution mentioned in the paper involves leveraging the rich semantic information encoded in pre-trained weights (WDP) and learning new domain weights (WDF) in a parameter-efficient manner. This is achieved by factorizing the fine-tuned weights for a new domain as follows: WdF ≈ WDP + ∆DF, where WDF represents unsupervised pre-trained weights learned from the new data distribution .


How were the experiments in the paper designed?

The experiments in the paper were designed to introduce ExPLoRA, a method for enhancing transfer learning of pre-trained vision transformers under domain shifts. The experiments involved initializing a ViT with pre-trained weights from large, natural-image datasets like DinoV2 or MAE, then continuing unsupervised pre-training on a new domain. In this extended pre-training phase, only 1-2 pre-trained ViT blocks and all normalization layers were unfrozen, while all other layers were tuned with LoRA. Finally, the resulting model was fine-tuned solely with LoRA on the new domain for supervised learning .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the EuroSAT dataset, which contains 27,000 13-band satellite images of 10 classes sourced from Sentinel-2 . The code for the dataset is open source, and the license for EuroSAT is provided at https://creativecommons.org/licenses/by/4.0/ .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The research introduces ExPLoRA, a novel pre-training strategy aimed at adapting pre-trained Vision Transformers for various visual domains like satellite imagery and medical data . The study conducts a comprehensive analysis on RGB, temporal, and multi-spectral satellite images, demonstrating either matching or outperforming existing methods that utilize full-rank pre-training from scratch . This indicates that ExPLoRA is effective in transferring knowledge from foundation models to different domains in a parameter-efficient manner, showcasing state-of-the-art supervised-learning performance with a fraction of the original ViT weights .

Furthermore, the results of the experiments, particularly in the context of linear probing accuracy, show significant improvements over prior state-of-the-art methods . The study reports an increase of over 7.3% in top-1 average accuracy compared to previous techniques, highlighting the robust unsupervised representations learned by ExPLoRA for the target domain without the need for expensive from-scratch pre-training . This improvement in performance, especially in linear probing, indicates the effectiveness of ExPLoRA in distilling knowledge from pre-trained models for different visual domains, showcasing successful transfer learning .

Moreover, the paper's broader impact section emphasizes the importance of techniques like PEFT, which can reduce the computational resources required for leveraging foundation models in various domains . By enabling researchers with limited computational resources to customize models for their specific needs, ExPLoRA accelerates the deployment and use of machine learning in critical domains such as sustainability and medicine . This aspect further supports the scientific hypotheses by demonstrating the practical implications and benefits of the proposed method in real-world applications .


What are the contributions of this paper?

The contributions of the paper "ExPLoRA: Parameter-Efficient Extended Pre-Training to Adapt Vision Transformers under Domain Shifts" include:

  1. Introducing ExPLoRA, a novel parameter-efficient method that extends unsupervised pre-training on target domains, achieving state-of-the-art supervised-learning performance using a fraction of the original ViT weights .
  2. Conducting a comprehensive case study on satellite imagery, showcasing improvements in linear probing top-1 accuracy and outperforming existing techniques on datasets like fMoW. The study also demonstrates generalization to other domains such as wildlife and medical imagery .

What work can be continued in depth?

Further research can delve into various aspects related to ExPLoRA that warrant deeper exploration. One area of interest is understanding the effectiveness of unfreezing a small number of blocks in combination with PEFT techniques like LoRA, which has shown promising results . Additionally, investigating the potential of other parameter-efficient methods to enhance the pre-training process with ExPLoRA could provide valuable insights . Furthermore, there is a need to evaluate the applicability of ExPLoRA in natural language domains and explore the possibility of eliminating the need to unfreeze a transformer block entirely .

Tables
1
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