Vision Transformer Continual Learning via Representation Replay
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Solution Overview
Problem
Current deep learning methods suffer from catastrophic forgetting, where neural networks forget previously learned information when adapting to new tasks, and existing solutions like experience replay face challenges with memory footprint and privacy concerns, especially in Vision Transformers for continual learning.
Innovation Solution
The method involves using a dual neural network setup where the first neural network learns a task and then becomes fixed, with the second neural network acting as a teacher to consolidate knowledge through exponential moving average, replaying internal representations instead of raw pixels to prevent forgetting and reduce memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If experience replay stores raw pixels of past experiences, then catastrophic forgetting is mitigated, but memory footprint increases and privacy concerns arise
Solution Approach 1:
The patent extracts only the essential feature representations from raw pixel data, storing compressed visual features instead of complete images. This extraction process removes redundant information while preserving the critical features needed for preventing catastrophic forgetting, thereby reducing memory footprint while maintaining reliability.
Solution Approach 2:
The patent creates simplified copies of visual experiences by storing processed feature representations rather than original pixel data. These feature copies contain the essential information needed for replay while occupying significantly less memory space, addressing both the forgetting mitigation and memory footprint concerns.
2Reliability
If experience replay stores raw pixels of past experiences, then catastrophic forgetting is mitigated, but privacy concerns increase
Solution Approach 1:
The patent extracts only the essential feature representations from raw pixel data, storing compressed visual features instead of complete images. This extraction process removes redundant information while preserving the critical features needed for preventing catastrophic forgetting, thereby reducing memory footprint while maintaining reliability.
Solution Approach 2:
The patent creates simplified copies of visual experiences by storing processed feature representations rather than original pixel data. These feature copies contain the essential information needed for replay while occupying significantly less memory space, addressing both the forgetting mitigation and memory footprint concerns.
3Reliability
If Vision Transformers are used for continual learning, then robustness and reliable predictions improve, but catastrophic forgetting occurs when adapting to new tasks
Solution Approach 1:
The patent performs preliminary consolidation of knowledge from previous tasks into the Vision Transformer model before adapting to new tasks. By pre-consolidating important features and representations, the model maintains its prediction reliability while being prepared for continual learning, preventing catastrophic forgetting through advance knowledge preservation.
Solution Approach 2:
The patent implements a feedback mechanism where the model replay experiences from past tasks and uses this replayed information to adjust and refine its predictions. This feedback loop ensures that knowledge from previous tasks continues to influence the model's behavior, preventing catastrophic forgetting while maintaining robustness and reliable predictions.
Data Source
AI summary
A computer-implemented method for continual task learning in a training framework. The method includes: providing a first deep neural network (θw) including a first function (Gw) and a second function (Fw) which are nested; providing a second deep neural network (θs) including a third function (Fs) as a counterpart to the second nested function (Fw); feeding input images to the first neural network (θw), such as through a filter and/or via patch embedding; generating representations of task samples using the first function (Gw); providing a memory (Dm) for storing at least some of the generated representations of task samples and/or having pre-stored task representation; providing the generated and memory stored representations of task samples to the second function (Fw); and providing memory stored representations of task samples to the third function (Fs).