Side-Tuning:domain adaption

时间 2021-08-15

标签 domain adaption Transfer learning 深度学习人工智能机器学习栏目 HTML 繁體版

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1、介绍 Side-Tuning: A Baseline for Network Adaptation via Additive Side Networks侧调（通常是网络自适应）的目标是利用预先训练的模型来更好地学习一个或多个新颖的任务。侧调方法很简单：它假设访问给定的（基本）模型B:X→Y，将输入X映射到某个表示Y上。然后，侧调学习一个侧模型S:X→Y，因此目标任务的计划表示如下 R（x）=B

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1. Domain Adaption 领域自适应
2. Code adaption for Pricing and Condition Technique
3. Adversarial Discriminative Domain Adaption 阅读笔记
4. 问题的转换(1): center loss, l2-constrained softmax loss, a noise adaption layer
5. 【论文笔记】Camera Style Adaption for Person Re-identification
6. 理解MAML：Model-Agnostic Meta-Learning for Fast Adaption of Deep Networks
7. 论文理解：Self-Ensembing with GAN-based Data Augmentation for Domain Adaption in Sematic Segmentation
8. Geodesic flow kernel for unsupervised domain adaptation
9. Content Aware ABR技术（五）
10. Interspeech 2017 | Self-adaptive Speech Recognition Technology

>>更多相关文章<<