Task-Aware Multi-Expert Architecture For Lifelong Deep Learning
This paper proposes TAME, a continual learning algorithm that leverages task similarity to guide expert selection and knowledge transfer for lifelong deep learning.
Why it matters
TAME's approach to continual learning could enable AI systems to learn and adapt over time without forgetting important prior knowledge, a key challenge in real-world AI applications.
Key Points
- 1TAME maintains a pool of pretrained neural networks and activates the most relevant expert for each new task
- 2A shared dense layer integrates features from the chosen expert to generate predictions
- 3TAME uses a replay buffer to store representative samples and embeddings from previous tasks and reuses them during training
- 4An attention mechanism prioritizes the most relevant stored information for each prediction
Details
The paper introduces TAME, a continual learning algorithm that aims to adapt flexibly to new tasks while retaining important knowledge across evolving task sequences. TAME maintains a pool of pretrained neural networks, or 'experts', and activates the most relevant expert for each new task. A shared dense layer integrates features from the chosen expert to generate predictions. To reduce catastrophic forgetting, TAME uses a replay buffer that stores representative samples and embeddings from previous tasks and reuses them during training. An attention mechanism further prioritizes the most relevant stored information for each prediction. Together, these components allow TAME to balance adaptation and retention in lifelong learning settings, as demonstrated by experiments on binary classification tasks derived from CIFAR-100.
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