Fetching the paper…
Reading the bibliography…
Supervised contrastive loss (SCL) is a competitive and often superior alternative to the cross-entropy loss for classification.
“Distance metric learning for large margin nearest neighbor classification”
Kilian Weinberger, John Blitzer and Lawrence Saul · 2005
Earlier work this paper cites.
“Improved deep metric learning with multi-class n-pair loss objective”
Kihyuk Sohn · 2016
Earlier work this paper cites.
“The full spectrum of deep net hessians at scale: Dynamics with sample size”
Vardan Papyan · 2018
Earlier work this paper cites.
“Contrastive multiview coding. arXiv”
Y Tian, D Krishnan and P Isola · 2019
Earlier work this paper cites.
“A simple framework for contrastive learning of visual representations”
Ting Chen, Simon Kornblith, Mohammad Norouzi and Geoffrey Hinton · 2020
Earlier work this paper cites.
“Supervised contrastive learning for pre-trained language model fine-tuning”
Beliz Gunel, Jingfei Du, Alexis Conneau and Ves Stoyanov · 2020
Earlier work this paper cites.
“Supervised contrastive learning”
Prannay Khosla et al · 2020
Earlier work this paper cites.
“Neural collapse with cross-entropy loss”
Jianfeng Lu and Stefan Steinerberger · 2020
Earlier work this paper cites.
“Neural collapse with unconstrained features”
Dustin Mixon, Hans Parshall and Jianzong Pi · 2020
Earlier work this paper cites.
“Prevalence of neural collapse during the terminal phase of deep learning training”
Vardan Papyan, XY Han and David Donoho · 2020
Earlier work this paper cites.
“Exploring deep neural networks via layer-peeled model: Minority collapse in imbalanced training”
Cong Fang, Hangfeng He, Qi Long and Weijie Su · 2021
Earlier work this paper cites.
“Dissecting supervised constrastive learning”
Florian Graf, Christoph Hofer, Marc Niethammer and Roland Kwitt · 2021
Earlier work this paper cites.
“Neural collapse under mse loss: Proximity to and dynamics on the central path”
XY Han, Vardan Papyan and David Donoho · 2021
Cited alongside, same era.
“An unconstrained layer-peeled perspective on neural collapse”
Wenlong Ji et al · 2021
Cited alongside, same era.
“Exploring balanced feature spaces for representation learning”
Bingyi Kang et al · 2021
Cited alongside, same era.
“Distributional robustness loss for long-tail learning”
Dvir Samuel and Gal Chechik · 2021
Cited alongside, same era.
“A Geometric Analysis of Neural Collapse with Unconstrained Features”
Zhihui Zhu et al · 2021
Cited alongside, same era.
“Are All Losses Created Equal: A Neural Collapse Perspective”
Jinxin Zhou et al · 2022
Later among the works it cites.
“Balanced contrastive learning for long-tailed visual recognition”
Jianggang Zhu et al · 2022
Later among the works it cites.
“On the Implicit Geometry of Cross-Entropy Parameterizations for Label-Imbalanced Data”
Tina Behnia, Ganesh Kini, Vala Vakilian and Christos Thrampoulidis · 2023
Closest in time.
“Mini-Batch Optimization of Contrastive Loss”
Jaewoong Cho et al · 2023
Closest in time.
“Neural Collapse in Deep Linear Network: From Balanced to Imbalanced Data”
Hien Dang et al · 2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Wittawat Jitkrittum, Aditya Menon, Ankit Rawat and Sanjiv Kumar · 2022
Cited alongside, same era.
“Targeted supervised contrastive learning for long-tailed recognition”
Tianhong Li et al · 2022
Cited alongside, same era.
“Extended unconstrained features model for exploring deep neural collapse”
Tom Tirer and Joan Bruna · 2022
Cited alongside, same era.
“Imbalance Trouble: Revisiting Neural-Collapse Geometry”
Christos Thrampoulidis, Ganesh Kini, Vala Vakilian and Tina Behnia · 2022
Cited alongside, same era.
“Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnable Classifier at the End of Deep Neural Network?”
Yibo Yang et al · 2022
Cited alongside, same era.
“Neural collapse with normalized features: A geometric analysis over the riemannian manifold”
Can Yaras et al · 2022
Cited alongside, same era.
Jinxin Zhou et al · 2022
Cited alongside, same era.
Peifeng Gao et al · 2023
Closest in time.
“Inducing Neural Collapse to a Fixed Hierarchy-Aware Frame for Reducing Mistake Severity”
Tong Liang and Jim Davis · 2023
Closest in time.
“Inducing Neural Collapse in Deep Long-tailed Learning”
Xuantong Liu et al · 2023
Closest in time.
“Learning Prototype Classifiers for Long-Tailed Recognition”
Saurabh Sharma, Yongqin Xian, Ning Yu and Ambuj Singh · 2023
Closest in time.
“Deep Neural Collapse Is Provably Optimal for the Deep Unconstrained Features Model”
Peter S\’uken\’k, Marco Mondelli and Christoph Lampert · 2023
Closest in time.
“Neural collapse inspired attraction-repulsion-balanced loss for imbalanced learning”
Liang Xie, Yibo Yang, Deng Cai and Xiaofei He · 2023
Closest in time.