Summary of May the Forgetting Be with You: Alternate Replay For Learning with Noisy Labels, by Monica Millunzi and Lorenzo Bonicelli and Angelo Porrello and Jacopo Credi and Petter N. Kolm and Simone Calderara
May the Forgetting Be with You: Alternate Replay for Learning with Noisy Labels
by Monica Millunzi, Lorenzo Bonicelli, Angelo Porrello, Jacopo Credi, Petter N. Kolm, Simone Calderara
First submitted to arxiv on: 26 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper addresses the challenge of forgetting in incremental training, a crucial aspect of continual learning (CL) in AI systems. The current approaches rely on replaying a restricted buffer of past data to overcome this issue. However, these strategies are vulnerable to noise in real-world scenarios where human annotation is limited or data is automatically gathered from the web. To tackle this problem, the authors introduce Alternate Experience Replay (AER), which leverages forgetting to maintain a clear distinction between clean, complex, and noisy samples in the memory buffer. The AER approach is equipped with Asymmetric Balanced Sampling (ABS), a new sample selection strategy that prioritizes purity on the current task while retaining relevant past samples. Experimental results demonstrate the effectiveness of this approach, achieving an average gain of 4.71% points in accuracy compared to existing loss-based purification strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about how AI systems can learn and remember new things without forgetting what they already know. This is important because AI systems are often trained on small amounts of data and then need to be updated with new information as it becomes available. The problem is that this process can be noisy, meaning the new information might not be accurate or relevant. To solve this issue, the authors propose a new approach called Alternate Experience Replay (AER), which separates new and old information into different categories based on how well they match what the AI system already knows. This approach is shown to improve accuracy by 4.71% points compared to other methods. |
Keywords
» Artificial intelligence » Continual learning
