Research map / Continual learning / Online and replay-based
Towards Better Plasticity-Stability Trade-Off in Incremental Learning: A Simple Linear Connector
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Combine a null-space-projected stability model and a current-task plasticity model with the linear connector | Incremental learning with ResNet-18 · 10-split-CIFAR-100 | ACC (%): 73.77 -> 79.79 | Helped |
| Combine a null-space-projected stability model and a current-task plasticity model with the linear connector | Incremental learning with ResNet-18 · 20-split-CIFAR-100 | ACC (%): 77.16 -> 80.80 | Helped |
| Combine a null-space-projected stability model and a current-task plasticity model with the linear connector | Incremental learning with ResNet-18 · 25-split-TinyImageNet | ACC (%): 58.28 -> 64.61 | Helped |
| Use the linear connector instead of Adam-NSCL | Incremental learning · 10-split-CIFAR-100 | BWT (%): -1.6 -> -0.92 | Helped |
| Use the linear connector instead of Adam-NSCL | Incremental learning · 20-split-CIFAR-100 | BWT (%): -3.66 -> -5.00 | Hurt |
| Use the linear connector instead of Adam-NSCL | Incremental learning · 25-split-TinyImageNet | BWT (%): -6.05 -> -6.00 | Helped |
| Use the linear connector instead of Adam-NSCL | Incremental learning · 10-split-CIFAR-100 | Intransigence measure I₁₀ (%): 14.50 -> 8.10 | Helped |
| Use the linear connector instead of Adam-NSCL | Incremental learning · 20-split-CIFAR-100 | Intransigence measure I₂₀ (%): 12.60 -> 7.00 | Helped |
| Use the linear connector instead of Adam-NSCL | Incremental learning · 25-split-TinyImageNet | Intransigence measure I₂₅ (%): 10.50 -> 5.75 | Helped |
| Set β to 0, selecting only the stability network (ablation) | Linear Connector · Incremental learning tasks shown in the β ablation | Accuracy on previous tasks | Helped |
| Set β to 1, selecting only the plasticity network (ablation) | Linear Connector · Incremental learning tasks shown in the β ablation | Accuracy on the current task | Helped |
| Set β close to 1/t to balance the two networks (ablation) | Linear Connector · 10-split-CIFAR-100 and 20-split-CIFAR-100 | Accuracy across tasks | Helped |
Closest papers by meaning
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- A Study of Plasticity Loss in On-Policy Deep Reinforcement Learning (NeurIPS 2024 · similarity 0.86)
- Revisiting Plasticity in Visual Reinforcement Learning: Data, Modules and Training Stages (ICLR 2024 · similarity 0.86)
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