Replacement Learning: Training Neural Networks with Fewer Parameters
End-to-end training with full-depth backpropagation remains the dominant paradigm for optimizing deep neural networks, but its efficiency deteriorates as models grow deeper. Since every block must be executed and differentiated under a single global objective, full-depth BP introduces substantial parameter redundancy, activation-memory cost, and training latency, especially when neighboring layers exhibit highly correlated learning patterns. Directly skipping or removing layers can reduce cost, but often weakens representation capacity or requires architecture-specific reuse designs. In this paper, we propose Replacement Learning (RepL), a training-time paradigm that reduces full-depth re...
| Authors | Yuming Zhang, Peizhe Wang, Tianyang Han et al. |
|---|---|
| Primary category | cs.CV |
| Published | 19 May 2026 |
| Last updated at source | 19 May 2026 |
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