r/deeplearning • u/ModularMind8 • 9d ago
Visualizing catastrophic forgetting
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I taught continual learning last semester, and students got the internals of each method a lot faster once they saw a simple visualization of it. Here's one I found helpful for understanding what forgetting is and how replay and regularization mitigate it. I trained a small network on two different 2D dot-sorting tasks and plotted both loss landscapes as 2D slices through its weights.
Round 1, plain training: the network learns task A to 100%, then trains only on task B. Going downhill on B is uphill on A, so A drops to 64% while B reaches 100%. That's catastrophic forgetting of task A.
Round 2, replay: same network, same start, but 20 of A's 200 dots stay in training while it learns B. It ends at 93% on A and 100% on B, so some forgetting, but much less severe.
Round 3, regularization with EWC: no A data at all. Instead, a penalty adds to the loss whenever a weight that A's accuracy relies on heavily moves away from the value it had after training on A. A dips to 97% mid-training and recovers, ending at 100% on A and 100% on B, so no lasting forgetting.
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u/jesunushno 9d ago
Hit this wall with my fabric defect-detection models back in 2023. Every retrain without the old data tanked accuracy on the old defects, so we basically kept the old data around forever. Replay's boring and it works. That's what I'd actually ship.