PatchRot Vision Transformer Training for Data Efficiency
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Solution Overview
Problem
Vision Transformers (ViTs) require large amounts of labeled data to outperform ConvNets, and existing self-supervised techniques are not optimized for ViTs, which lack inductive biases like translation equivariance and locality, making them less effective with limited data.
Innovation Solution
The PatchRot technique trains ViTs to predict rotation angles of images and image patches using new MLP heads, learning both global and local features in a self-supervised manner, which alleviates the need for extensive labeled data and leverages the unique processing nature of ViTs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If ViTs are trained with existing self-supervised techniques, then they can learn features without labeled data, but their performance is inferior to ConvNets due to lack of inductive bias
Solution Approach 1:
The patent segments the image processing task into two distinct prediction tasks: global rotation prediction (using the entire image) and local rotation prediction (using image patches). This segmentation allows the ViT to learn both global and local inductive biases separately, with the global head capturing translation equivariance and the local head capturing locality, thereby improving performance while using unlabeled data
Solution Approach 2:
The patent adds a new dimension to the feature learning space by introducing dual prediction heads (global and local) that operate at different spatial scales. This dimensional expansion allows the model to simultaneously learn multiple types of inductive biases that are normally acquired through supervised training with labeled data
2Adaptability or versatility
If ViTs process images by dividing into patches and applying self-attention, then they can capture global context, but they lose the inductive bias of translation equivariance and locality
Solution Approach 1:
The patent segments the feature learning process into global feature extraction (through self-attention on all patches) and local feature extraction (through dedicated local prediction heads operating on individual patches). This segmentation enables the ViT to maintain its global context capability while simultaneously learning local inductive biases that ConvNets naturally possess
Solution Approach 2:
The patent performs preliminary self-supervised training on unlabeled data using rotation prediction tasks before fine-tuning on downstream tasks. This preliminary action allows the ViT to pre-acquire inductive biases (translation equivariance and locality) without labeled data, improving its performance on subsequent supervised tasks
Data Source
AI summary
Examples of computer-implemented training techniques are described and tailor-made for Vision Transformers (ViTs). Example techniques include training a model (network) for the rotation of images and image patches and training of the model to predict the rotation angles. The model learns to extract both global and local features from an image.