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title: Adversarial Feature Hallucination Networks for Few-Shot Learning

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# Adversarial Feature Hallucination Networks for Few-Shot Learning


- this work mainly focuses on data augmentation.
- the basic assumption of this approach is that **the intra-class cross-sample relationship learned from seen classes can be applied to unseen classes.**
- in data augmentation, **the diversity and discriminabilit** are especially important in the few-shot setting.

## Common Problems in the Data Augmentation Approach

- learning arbitary transformation mappings may destroy discriminatability of the synthesized samples.
- when synthesizing samples specifically for certain tasks (regularize the synthesis process), the task may constrain the synthesis processs and thus the synthesized smaples tend to collapse into certain modes. 
- the proposed cWGAN-based feature synthesis framework and two novel regularizers.
- the 

