Asymmetric Loss For Multi-Label Classification

29 September 2020

Papers citing "Asymmetric Loss For Multi-Label Classification"

8 / 258 papers shown

Title
MlTr: Multi-label Classification with Transformer Xingyi Cheng Hezheng Lin Xiangyu Wu Fan Yang Dong Shen Zhongyuan Wang Nian Shi Honglin Liu ViT 20 48 0 11 Jun 2021
Imperceptible Adversarial Examples for Fake Image Detection Quanyu Liao Yuezun Li Xiaoqiang Guo Bin Kong Yingxin Zhu Jianlei Liu Zhuqing Jiang Qi Song Xi Wu AAML 100 33 0 03 Jun 2021
Semantic Diversity Learning for Zero-Shot Multi-label Classification Avi Ben-Cohen Nadav Zamir Emanuel Ben-Baruch Itamar Friedman Lihi Zelnik-Manor VLM 19 30 0 12 May 2021
ImageNet-21K Pretraining for the Masses T. Ridnik Emanuel Ben-Baruch Asaf Noy Lihi Zelnik-Manor SSeg VLM CLIP 187 690 0 22 Apr 2021
Sewer-ML: A Multi-Label Sewer Defect Classification Dataset and Benchmark Joakim Bruslund Haurum T. Moeslund 20 60 0 19 Mar 2021
Evaluating Multi-label Classifiers with Noisy Labels Wenting Zhao Carla P. Gomes NoLa 74 14 0 16 Feb 2021
Re-labeling ImageNet: from Single to Multi-Labels, from Global to Localized Labels Sangdoo Yun Seong Joon Oh Byeongho Heo Dongyoon Han Junsuk Choe Sanghyuk Chun 414 142 0 13 Jan 2021
A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay L. Smith 208 1,020 0 26 Mar 2018