ICLR2020 cross-domain few-shot classification via learned feature-wise transformation
跨域小样本分类 paper code 摘要 摘要：小样本分类旨在识别每个类别中只有少数标记图像的新类别。现有的基于度量的小样本分类算法通过使用学习度量函数将查询图像的特征嵌入与少数标记图像(支持示例)的特征嵌入进行比较来预测类别。虽然已经证明了这些方法有很好的性能，但是由于域之间的特征分布存在很大的差异，这些方法往往不能推广到不可见的域。在这项工作中，我们解决了基于度量的方法在领域转移下的少样本分
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相关文章
- 1. 【论文阅读】[meta learning]cross-domain few-shot classification via learned feature-wise transformation.
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- 4. Unsupervised Person Re-identification via Multi-label Classification
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- 8. 【图像篡改 ECCV2018】Fighting Fake News: Image Splice Detection via Learned Self-Consistency
- 9. 论文笔记-Depth Estimation via Affinity Learned with Convolutional Spatial Propagation Network
- 10. Relation Classification via Convolutional Deep Neural Network 论文笔记