Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Self-Augmentation: Generalizing Deep Networks to Unseen Classes for
  Few-Shot Learning   Few-shot learning aims to classify unseen classes with a few training
examples. While recent works have shown that standard mini-batch training with
a carefully designed training strategy can improve generalization ability for
unseen classes, well-known problems in deep networks such as memorizing
training statistics have been less explored for few-shot learning. To tackle
this issue, we propose self-augmentation that consolidates self-mix and
self-distillation. Specifically, we exploit a regional dropout technique called
self-mix, in which a patch of an image is substituted into other values in the
same image. Then, we employ a backbone network that has auxiliary branches with
its own classifier to enforce knowledge sharing. Lastly, we present a local
representation learner to further exploit a few training examples for unseen
classes. Experimental results show that the proposed method outperforms the
state-of-the-art methods for prevalent few-shot benchmarks and improves the
generalization ability.

18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
28
The categories of this paper are: alg-geom
25
The categories of this paper are: patt-sol
17
The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph