Explore the Power of Dropout on Few-shot Learning

Rui Zhao; Shaobo Lin; Xingyu Zeng

REVIEW

Explore the Power of Dropout on Few-shot Learning

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2301.11015 v1 pith:Q7V7KEU7 submitted 2023-01-26 cs.CV

Explore the Power of Dropout on Few-shot Learning

Shaobo Lin , Xingyu Zeng , Rui Zhao This is my paper

classification cs.CV

keywords few-shotlearningdropoutpowerdeepexploreclassificationcoco

verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

0 comments

read the original abstract

The generalization power of the pre-trained model is the key for few-shot deep learning. Dropout is a regularization technique used in traditional deep learning methods. In this paper, we explore the power of dropout on few-shot learning and provide some insights about how to use it. Extensive experiments on the few-shot object detection and few-shot image classification datasets, i.e., Pascal VOC, MS COCO, CUB, and mini-ImageNet, validate the effectiveness of our method.

Explore the Power of Dropout on Few-shot Learning

discussion (0)