GradNorm：Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks，梯度归一化
文章目录 引言 gradnorm gradnorm论文地址：https://arxiv.org/abs/1711.02257html gradnorm是一种优化方法，在多任务学习（Multi-Task Learning）中，解决 1. 不一样任务loss梯度的量级（magnitude）不一样，形成有的task在梯度反向传播中占主导地位，模型过度学习该任务而忽视其它任务；2. 不一样任务收敛
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相关文章
- 1. Adaptive Loss-aware Quantization for Multi-bit Networks
- 2. Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
- 3. 归一化（Batch Normalization & Group Normalization）
- 4. Deep Learning -- Normalization
- 5. TRINI: an adaptive load balancing strategy
- 6. 深度学习：批归一化Batch Normalization
- 7. Flow Prediction in Spatio-Temporal Networks Based on Multitask Deep Learning
- 8. Normalization 归一化方法 BN LN IN GN
- 9. Adaptive Document Retrieval for Deep Question Answering
- 10. 批量归一化——Batch Normalization