Explore the Power of Dropout on Few-shot Learning

26 January 2023

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

11 / 11 papers shown

Title
FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding Bo Sun Banghuai Li Shengcai Cai Ye Yuan Chi Zhang 113 376 0 10 Mar 2021
Multi-Scale Positive Sample Refinement for Few-Shot Object Detection Jiaxi Wu Songtao Liu Di Huang Yunhong Wang ObjD 128 285 0 18 Jul 2020
Frustratingly Simple Few-Shot Object Detection Xin Wang Thomas E. Huang Trevor Darrell Joseph E. Gonzalez Feng Yu ObjD 221 555 0 16 Mar 2020
Few-shot Object Detection via Feature Reweighting Bingyi Kang Zhuang Liu Xin Wang Feng Yu Jiashi Feng Trevor Darrell ObjD OffRL 94 699 0 05 Dec 2018
DropBlock: A regularization method for convolutional networks Golnaz Ghiasi Nayeon Lee Quoc V. Le 115 914 0 30 Oct 2018
Few-Shot Learning with Metric-Agnostic Conditional Embeddings Nathan Hilliard Lawrence Phillips Scott Howland A. Yankov Court D. Corley Nathan Oken Hodas SSL 72 159 0 12 Feb 2018
Learning to Compare: Relation Network for Few-Shot Learning Flood Sung Yongxin Yang Li Zhang Tao Xiang Philip Torr Timothy M. Hospedales 304 4,049 0 16 Nov 2017
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 827 11,943 0 09 Mar 2017
Feature Pyramid Networks for Object Detection Nayeon Lee Piotr Dollár Ross B. Girshick Kaiming He Bharath Hariharan Serge J. Belongie ObjD 483 22,143 0 09 Dec 2016
Matching Networks for One Shot Learning Oriol Vinyals Charles Blundell Timothy Lillicrap Koray Kavukcuoglu Daan Wierstra VLM 375 7,333 0 13 Jun 2016
R-FCN: Object Detection via Region-based Fully Convolutional Networks Jifeng Dai Yi Li Kaiming He Jian Sun ObjD 177 5,642 0 20 May 2016