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Temporal Imbalance of Positive and Negative Supervision in Class-Incremental Learning
🤖AI Summary
Researchers at arXiv have identified temporal imbalance as a key factor causing catastrophic forgetting in Class-Incremental Learning (CIL) systems. They propose Temporal-Adjusted Loss (TAL), a new method that uses temporal decay kernels to reweight negative supervision, demonstrating significant improvements in reducing forgetting across multiple CIL benchmarks.
Key Takeaways
- →Temporal imbalance, where earlier classes receive stronger negative supervision toward the end of training, is identified as a key cause of prediction bias in Class-Incremental Learning.
- →The proposed Temporal-Adjusted Loss (TAL) method uses temporal decay kernels to dynamically reweight negative supervision in cross-entropy loss.
- →TAL degenerates to standard cross-entropy under balanced conditions while effectively mitigating prediction bias under imbalance.
- →Extensive experiments show TAL significantly reduces catastrophic forgetting and improves performance on multiple CIL benchmarks.
- →The research emphasizes the importance of temporal modeling for achieving stable long-term learning in AI systems.
#class-incremental-learning#catastrophic-forgetting#temporal-imbalance#deep-learning#machine-learning#arxiv#research#temporal-adjusted-loss#ai-training
Read Original →via arXiv – CS AI
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