Unsqueeze [CLS] Bottleneck to Learn Rich Representations

24 July 2024

Papers citing "Unsqueeze [CLS] Bottleneck to Learn Rich Representations"

8 / 8 papers shown

Title
Masked Autoencoders Are Scalable Vision Learners Kaiming He Xinlei Chen Saining Xie Yanghao Li Piotr Dollár Ross B. Girshick ViT TPM 258 7,337 0 11 Nov 2021
Emerging Properties in Self-Supervised Vision Transformers Mathilde Caron Hugo Touvron Ishan Misra Hervé Jégou Julien Mairal Piotr Bojanowski Armand Joulin 283 5,723 0 29 Apr 2021
S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration Zhiqiang Shen Zechun Liu Jie Qin Lei Huang Kwang-Ting Cheng Marios Savvides UQCV SSL MQ 244 22 0 17 Feb 2021
SEED: Self-supervised Distillation For Visual Representation Zhiyuan Fang Jianfeng Wang Lijuan Wang Lei Zhang Yezhou Yang Zicheng Liu SSL 231 186 0 12 Jan 2021
BYOL works even without batch statistics Pierre Harvey Richemond Jean-Bastien Grill Florent Altché Corentin Tallec Florian Strub ... Samuel L. Smith Soham De Razvan Pascanu Bilal Piot Michal Valko SSL 242 114 0 20 Oct 2020
Improved Baselines with Momentum Contrastive Learning Xinlei Chen Haoqi Fan Ross B. Girshick Kaiming He SSL 235 3,029 0 09 Mar 2020
Boosting Self-Supervised Learning via Knowledge Transfer M. Noroozi Ananth Vinjimoor Paolo Favaro Hamed Pirsiavash SSL 207 282 0 01 May 2018
Feature Pyramid Networks for Object Detection Tsung-Yi Lin Piotr Dollár Ross B. Girshick Kaiming He Bharath Hariharan Serge J. Belongie ObjD 154 3,574 0 09 Dec 2016