Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adversarial Contrastive Self-Supervised Learning   Recently, learning from vast unlabeled data, especially self-supervised
learning, has been emerging and attracted widespread attention. Self-supervised
learning followed by the supervised fine-tuning on a few labeled examples can
significantly improve label efficiency and outperform standard supervised
training using fully annotated data. In this work, we present a novel
self-supervised deep learning paradigm based on online hard negative pair
mining. Specifically, we design a student-teacher network to generate
multi-view of the data for self-supervised learning and integrate hard negative
pair mining into the training. Then we derive a new triplet-like loss
considering both positive sample pairs and mined hard negative sample pairs.
Extensive experiments demonstrate the effectiveness of the proposed method and
its components on ILSVRC-2012.

24
The categories of this paper are: dg-ga
31
The categories of this paper are: ao-sci
35
The categories of this paper are: mtrl-th
19
The categories of this paper are: eess
28
The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
32
The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat
6
The categories of this paper are: astro-ph
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph