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Explore the Power of Dropout on Few-shot Learning

Explore the Power of Dropout on Few-shot Learning

26 January 2023
Shaobo Lin
Xingyu Zeng
Rui Zhao
ArXivPDFHTML

Papers citing "Explore the Power of Dropout on Few-shot Learning"

5 / 5 papers shown
Title
Hallucination Improves Few-Shot Object Detection
Hallucination Improves Few-Shot Object Detection
Weilin Zhang
Yu-xiong Wang
ObjD
50
109
0
04 May 2021
Free Lunch for Few-shot Learning: Distribution Calibration
Free Lunch for Few-shot Learning: Distribution Calibration
Shuo Yang
Lu Liu
Min Xu
OODD
196
316
0
16 Jan 2021
Frustratingly Simple Few-Shot Object Detection
Frustratingly Simple Few-Shot Object Detection
Xin Wang
Thomas E. Huang
Trevor Darrell
Joseph E. Gonzalez
F. I. F. Richard Yu
ObjD
75
535
0
16 Mar 2020
Meta R-CNN : Towards General Solver for Instance-level Few-shot Learning
Meta R-CNN : Towards General Solver for Instance-level Few-shot Learning
Xiaopeng Yan
Ziliang Chen
Anni Xu
Xiaoxi Wang
Xiaodan Liang
Liang Lin
ObjD
151
440
0
28 Sep 2019
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Chelsea Finn
Pieter Abbeel
Sergey Levine
OOD
237
11,568
0
09 Mar 2017
1