i-Mix: A Domain-Agnostic Strategy for Contrastive Representation
Learning

i-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning

17 October 2020

Chun-Liang Li

Papers citing "i-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning"

7 / 7 papers shown

Title
Data Augmentation by Selecting Mixed Classes Considering Distance Between Classes Shungo Fujii Yasunori Ishii Kazuki Kozuka Tsubasa Hirakawa Takayoshi Yamashita H. Fujiyoshi 34 2 0 12 Sep 2022
Towards Understanding the Data Dependency of Mixup-style Training Muthuraman Chidambaram Xiang Wang Yuzheng Hu Chenwei Wu Rong Ge UQCV 41 24 0 14 Oct 2021
Survey: Image Mixing and Deleting for Data Augmentation Humza Naveed Saeed Anwar Munawar Hayat Kashif Javed Ajmal Mian 30 78 0 13 Jun 2021
BYOL for Audio: Self-Supervised Learning for General-Purpose Audio Representation Daisuke Niizumi Daiki Takeuchi Yasunori Ohishi N. Harada K. Kashino SSL 24 175 0 11 Mar 2021
Improved Baselines with Momentum Contrastive Learning Xinlei Chen Haoqi Fan Ross B. Girshick Kaiming He SSL 267 3,369 0 09 Mar 2020
Multi-task self-supervised learning for Robust Speech Recognition Mirco Ravanelli Jianyuan Zhong Santiago Pascual P. Swietojanski João Monteiro J. Trmal Yoshua Bengio SSL 183 288 0 25 Jan 2020
Boosting Self-Supervised Learning via Knowledge Transfer M. Noroozi Ananth Vinjimoor Paolo Favaro Hamed Pirsiavash SSL 209 292 0 01 May 2018