Efficient Few-Shot Distillation of Vision Transformers through Weight Copying and Low-Rank Adaptation

Centrala begrepp

A novel few-shot feature distillation approach for vision transformers based on intermittent weight copying and enhanced low-rank adaptation, which enables efficient training of lightweight transformer models on limited data.

Sammanfattning

The paper proposes a novel few-shot feature distillation approach for vision transformers, called WeCoLoRA, which consists of two key steps:

1. Leveraging the consistent depth-wise structure of vision transformers, the authors first copy the weights from intermittent layers of existing pre-trained vision transformers (teachers) into shallower architectures (students), where the intermittence factor controls the complexity of the student transformer with respect to its teacher.

2. The authors then employ an enhanced version of Low-Rank Adaptation (LoRA) to distill knowledge into the student in a few-shot scenario, aiming to recover the information processing carried out by the skipped teacher layers.

The authors present comprehensive experiments with supervised and self-supervised transformers as teachers, on five data sets from various domains, including natural, medical and satellite images. The results confirm the superiority of the proposed WeCoLoRA approach over competitive baselines. The ablation study demonstrates the usefulness of each component of the proposed pipeline.

The authors also analyze the features learned by the distilled models, showing that WeCoLoRA generates more robust and discriminative features compared to the strongest competitor.

Statistik

"The accuracy tends to grow as the model gets larger [19,49], most of the attention has been dedicated to building larger and more powerful models."
"Large amounts of data are not always available in some domains, e.g. hyperspectral image segmentation [30]."
"Our approach is divided into two steps. For the first step, we leverage the fact that vision transformers have a consistent depth-wise structure, i.e. the input and output dimensions are compatible across transformer blocks."
"We perform the pre-training stage of efficient student transformers via few-shot knowledge distillation on various subsets of ImageNet-1K [15]."

Citat

"Few-shot knowledge distillation (FSKD) paradigm [24,39,51,53,55,58,59, 76, 79], which was explored in both language [53, 58, 79] and vision [24, 39,41, 42, 51, 59, 76] domains."
"We emphasize that FSKD has not been extensively explored in the vision domain [24, 39, 41, 42, 51, 55, 59, 76], with even less studies focused on the pre-training stage of vision transformers [42]."
"We present few-shot and linear probing experiments on five benchmark data sets comprising natural, medical and satellite images, demonstrating the utility of our training pipeline across different domains."

Viktiga insikter från

Weight Copy and Low-Rank Adaptation for Few-Shot Distillation of Vision Transformers

by Diana-Nicole... på arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09326.pdf

Djupare frågor

How can the weight copying mechanism be generalized to other neural network architectures beyond transformers

The weight copying mechanism can be generalized to other neural network architectures by introducing adaptor blocks that can reshape the copied weights to fit the architecture of the student model. These adaptor blocks would need to be tailored for each specific pair of teacher and student models to ensure compatibility. By incorporating these adaptor blocks, the weight copying mechanism can be extended to various architectures beyond transformers, allowing for efficient knowledge distillation in a wide range of neural network models.

What are the potential limitations of the enhanced LoRA approach in handling large distribution shifts between the teacher and student models

One potential limitation of the enhanced LoRA approach in handling large distribution shifts between the teacher and student models is the risk of overfitting. When there are significant distribution shifts, the enhanced LoRA may struggle to adapt and generalize effectively, leading to overfitting on the limited data available for knowledge distillation. Additionally, the complexity of the enhanced LoRA approach may make it more susceptible to performance degradation in scenarios with large distribution shifts, as the model may struggle to capture the underlying patterns and information processing carried out by the teacher model.

How can the proposed framework be extended to handle multi-modal data, such as combining vision and language, for few-shot knowledge distillation

To extend the proposed framework to handle multi-modal data, such as combining vision and language for few-shot knowledge distillation, a fusion approach can be implemented. This fusion approach would involve integrating both vision and language modalities into the teacher model and distilling the knowledge from this multi-modal teacher to a student model. The student model would need to be designed to effectively process and learn from the combined vision and language information. Additionally, specialized attention mechanisms and fusion layers can be incorporated into the student model to enable effective learning from both modalities simultaneously. By adapting the framework to accommodate multi-modal data, the approach can be applied to diverse tasks that require knowledge distillation from multiple sources of information.