Vision Transformer Self-Distillation for Domain Generalization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Vision transformers (ViTs) suffer from performance degradation when facing out-of-distribution target domain data, limiting their effectiveness in domain generalization tasks.
Innovation Solution
A self-distillation strategy is employed for ViTs, which provides soft supervision to intermediate blocks to enhance internal representations and alleviate overfitting, thereby promoting the learning of cross-domain generalizable features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vision transformers are trained using standard supervised learning methods, then they achieve good performance on in-distribution data, but they suffer from performance degradation when facing out-of-distribution target domain data
Solution Approach 1:
The patent applies self-distillation where the vision transformer model trains itself by using its own intermediate block outputs as training data. The model generates pseudo-labels from intermediate representations and uses these to train the final classifier, enabling the system to learn from itself rather than requiring external annotated data for each domain. This self-service mechanism improves domain generalization by allowing the model to discover domain-invariant features autonomously.
Solution Approach 2:
The patent performs preliminary action by training intermediate transformer blocks first before training the final classifier. The intermediate blocks are trained to produce meaningful representations that can be used for domain generalization. This preliminary training of representation layers before the classification head enables the model to build a foundation of domain-invariant features that generalize to out-of-distribution data.
2Measurement precision
If the vision transformer uses multiple transformer blocks to process images, then it captures global context effectively, but it becomes computationally expensive and complex
Solution Approach 1:
The patent segments the vision transformer into multiple independent transformer blocks that can be trained separately. Each block processes a portion of the image and produces intermediate representations. This segmentation allows the model to capture global context through multiple passes while maintaining computational efficiency by processing data in smaller, manageable chunks rather than requiring a single monolithic large transformer.
Solution Approach 2:
The patent implements a nested structure where intermediate transformer blocks are embedded within the overall architecture and can be reused. The same transformer blocks serve multiple purposes: they extract features, generate pseudo-labels for self-distillation, and contribute to the final classification. This nesting reduces redundancy and computational complexity by sharing common representation learning across different levels of the hierarchy.
3Adaptability or versatility
If the vision transformer is trained on diverse domains, then it improves domain generalization, but collecting and processing such diverse data becomes expensive and complex
Solution Approach 1:
The model performs self-service by generating its own training data through self-distillation. Instead of requiring external diverse datasets for training, the vision transformer uses its own intermediate block outputs to create pseudo-labels and training examples. This self-generated data captures domain-invariant features without requiring manual curation or collection of diverse real-world data, significantly reducing data processing complexity.
Solution Approach 2:
The patent creates synthetic copies of training data by using intermediate block outputs as pseudo-labels. These copied representations capture the essential domain-invariant features without requiring actual diverse data samples. The model learns from these synthesized representations, achieving domain generalization without the need to collect, store, and process extensive diverse datasets, thereby reducing infrastructure and computational requirements.
Data Source
AI summary
An apparatus and method for a machine learning engine for domain generalization which trains a vision transformer neural network using a training dataset including at least two domains for diagnosis of a medical condition. Image patches and class tokens are processed through a sequence of feature extraction transformer blocks to obtain a predicted class token. In parallel, intermediate class tokens are extracted as outputs of each of the feature extraction transformer blocks, where each transformer block is a sub-model. One sub-model is randomly sampled from the sub-models to obtain a sampled intermediate class token. The intermediate class token is used to make a sub-model prediction. The vision transformer neural network is optimized based on a difference between the predicted class token and the sub-model prediction. Inferencing is performed for a target medical image in a target domain that is different from the at least two domains.