Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few
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Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few Labels

21 February 2023

Jun Zhu

Papers citing "Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few Labels"

10 / 10 papers shown

Title
Equivariant Energy-Guided SDE for Inverse Molecular Design Fan Bao Min Zhao Zhongkai Hao Pei‐Yun Li Chongxuan Li Jun Zhu DiffM 147 38 0 30 Sep 2022
FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning Yidong Wang Hao Chen Qiang Heng Wenxin Hou Yue Fan ... Marios Savvides T. Shinozaki Bhiksha Raj Bernt Schiele Xing Xie 152 169 0 15 May 2022
StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets Axel Sauer Katja Schwarz Andreas Geiger 160 354 0 01 Feb 2022
Masked Autoencoders Are Scalable Vision Learners Kaiming He Xinlei Chen Saining Xie Yanghao Li Piotr Dollár Ross B. Girshick ViT TPM 233 5,353 0 11 Nov 2021
FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling Bowen Zhang Yidong Wang Wenxin Hou Hao Wu Jindong Wang Manabu Okumura T. Shinozaki AAML 180 673 0 15 Oct 2021
Unsupervised Selective Labeling for More Effective Semi-Supervised Learning Xudong Wang Long Lian Stella X. Yu 158 25 0 06 Oct 2021
Instance-Conditioned GAN Arantxa Casanova Marlene Careil Jakob Verbeek M. Drozdzal Adriana Romero Soriano GAN 160 112 0 10 Sep 2021
Emerging Properties in Self-Supervised Vision Transformers Mathilde Caron Hugo Touvron Ishan Misra Hervé Jégou Julien Mairal Piotr Bojanowski Armand Joulin 257 4,299 0 29 Apr 2021
Meta Pseudo Labels Hieu H. Pham Zihang Dai Qizhe Xie Minh-Thang Luong Quoc V. Le VLM 230 583 0 23 Mar 2020
There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average Ben Athiwaratkun Marc Finzi Pavel Izmailov A. Wilson 168 232 0 14 Jun 2018