Feature Dropout Knowledge Distillation for Transformer to CNN Transfer
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Solution Overview
Problem
Existing knowledge distillation methods face challenges in bridging the 'capacity gap' when transferring knowledge from complex Transformer models to lightweight convolutional neural networks (CNNs), due to structural and inductive bias differences between these model types.
Innovation Solution
The proposed solution involves feature dropout knowledge distillation, which uses a sparse principal component analysis framework to eliminate redundant structural information from the features of both teacher and student models. Additionally, logit dropout distillation is employed to create principled uncertainty estimates, allowing for more effective knowledge transfer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If knowledge is distilled from a larger Transformer model to a smaller CNN model, then memory and computation requirements are reduced, but accuracy is lost due to structural differences and redundant elements
Solution Approach 1:
The patent extracts only the essential knowledge from the teacher model's features by applying dropout to remove redundant structural information. The student model learns from these pruned features, acquiring core knowledge while discarding model-specific redundancies, thus maintaining accuracy while reducing resource requirements.
Solution Approach 2:
The patent changes the parameter representation by transforming features through dropout with specific probabilities (e.g., p=0.5). This parameter transformation allows the student model to learn robust representations that are invariant to structural differences, bridging the capacity gap between Transformer and CNN architectures.
2Productivity
If dropout is applied to remove redundant structural information, then knowledge transfer effectiveness is improved, but information loss may occur
Solution Approach 1:
The patent applies partial dropout (not complete removal) where features are dropped with probability p<1. This partial action allows the model to retain essential information while removing redundancies. The student model receives sufficient information to learn effectively without being overwhelmed by structural noise.
Solution Approach 2:
The patent uses knowledge distillation loss as feedback to guide the student model learning process. The student model's predictions are compared against the teacher model's soft labels, providing continuous feedback that compensates for information lost during dropout, ensuring accurate knowledge transfer despite feature pruning.
3Adaptability or versatility
If feature dropout distillation is used to eliminate structural redundancy, then the capacity gap between models is reduced, but training complexity increases
Solution Approach 1:
The patent segments the training process into distinct components: feature extraction, dropout application, and knowledge distillation loss computation. This segmentation allows each component to be optimized independently, managing training complexity while achieving model compatibility across different architectures.
Data Source
AI summary
Embodiments described herein provide systems and methods for knowledge distillation. A system encodes features from a student model and a teacher model to provide principal components. The principal components may be decoded to provide decoded components. Logits may also be output by the student and teacher models. A loss function may be computed based on the principal components, decoded components, and logits. Parameters of the student model may be updated based on the loss function.