Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Self-Supervised Class-Cognizant Few-Shot Classification   Unsupervised learning is argued to be the dark matter of human intelligence.
To build in this direction, this paper focuses on unsupervised learning from an
abundance of unlabeled data followed by few-shot fine-tuning on a downstream
classification task. To this aim, we extend a recent study on adopting
contrastive learning for self-supervised pre-training by incorporating
class-level cognizance through iterative clustering and re-ranking and by
expanding the contrastive optimization loss to account for it. To our
knowledge, our experimentation both in standard and cross-domain scenarios
demonstrate that we set a new state-of-the-art (SoTA) in (5-way, 1 and 5-shot)
settings of standard mini-ImageNet benchmark as well as the (5-way, 5 and
20-shot) settings of cross-domain CDFSL benchmark. Our code and experimentation
can be found in our GitHub repository: https://github.com/ojss/c3lr.

35
The categories of this paper are: mtrl-th
20
The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
16
The categories of this paper are: stat
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
17
The categories of this paper are: q-fin
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The categories of this paper are: cmp-lg
8
The categories of this paper are: hep-ex
11
The categories of this paper are: hep-lat
5
The categories of this paper are: gr-qc
31
The categories of this paper are: ao-sci
7
The categories of this paper are: hep-th