Decomposing internal policies in transformer-based vision models enhances image classification performance by optimizing feature extraction layers.

Motivation

The concept of internal policy decomposition has shown promise in optimizing reasoning in language models. Applying a similar technique to vision models could lead to improvements in feature extraction and classification tasks by optimizing the internal policies of the model's layers.

Proposed Method

Implement policy decomposition techniques on a vision transformer model by identifying and optimizing internal policies within its layers. Conduct experiments on standard image classification datasets such as ImageNet to evaluate improvements in accuracy and efficiency. Compare results with baseline models that do not utilize policy decomposition.

Expected Contribution

This study would demonstrate the applicability of internal policy decomposition beyond language models, potentially leading to improved methods for enhancing performance in vision tasks.

Required Resources

Access to large-scale image datasets, computational resources for training vision transformers, and expertise in computer vision and machine learning.

Source Paper

Bottom-up Policy Optimization: Your Language Model Policy Secretly Contains Internal Policies

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