Stochastic Weight Averaging in PyTorch
Stochastic Weight Averaging in PyTorch SWA爲什麼有效 Figure 1. Illustrations of SWA and SGD with a Preactivation ResNet-164 on CIFAR-100 [1]. Left: test error surface for three FGE samples and the correspo
相關文章
- 1. 《SWALP：Stochastic Weight Averaging in Low-Precision Training》
- 2. 目標檢測之五：隨機權值平均（Stochastic Weight Averaging，SWA）---木有看懂
- 3. Quaternion Weighted Averaging Proofs
- 4. pytorch查看模型weight與grad
- 5. Stochastic Computing + Quantization
- 6. LSTM in Pytorch
- 7. DECOUPLED WEIGHT DECAY REGULARIZATION
- 8. Random/Stochastic
- 9. Stochastic Gradient Descent
- 10. Install PyTorch in windows （anaconda）
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相關文章
- 1. 《SWALP：Stochastic Weight Averaging in Low-Precision Training》
- 2. 目標檢測之五：隨機權值平均（Stochastic Weight Averaging，SWA）---木有看懂
- 3. Quaternion Weighted Averaging Proofs
- 4. pytorch查看模型weight與grad
- 5. Stochastic Computing + Quantization
- 6. LSTM in Pytorch
- 7. DECOUPLED WEIGHT DECAY REGULARIZATION
- 8. Random/Stochastic
- 9. Stochastic Gradient Descent
- 10. Install PyTorch in windows （anaconda）