Benchmarking Low-Shot Robustness to Natural Distribution Shifts

21 April 2023

Papers citing "Benchmarking Low-Shot Robustness to Natural Distribution Shifts"

10 / 10 papers shown

Title
What Do Self-Supervised Vision Transformers Learn? Namuk Park Wonjae Kim Byeongho Heo Taekyung Kim Sangdoo Yun SSL 65 76 1 01 May 2023
Few-Shot Adaptation of Pre-Trained Networks for Domain Shift Wenyu Zhang Li Shen Wanyue Zhang Chuan-Sheng Foo TTA 39 17 0 30 May 2022
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
Are Transformers More Robust Than CNNs? Yutong Bai Jieru Mei Alan Yuille Cihang Xie ViT AAML 170 256 0 10 Nov 2021
A Fine-Grained Analysis on Distribution Shift Olivia Wiles Sven Gowal Florian Stimberg Sylvestre-Alvise Rebuffi Ira Ktena Krishnamurthy Dvijotham A. Cemgil OOD 215 196 0 21 Oct 2021
Intriguing Properties of Vision Transformers Muzammal Naseer Kanchana Ranasinghe Salman Khan Munawar Hayat F. Khan Ming-Hsuan Yang ViT 248 618 0 21 May 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
ImageNet-21K Pretraining for the Masses T. Ridnik Emanuel Ben-Baruch Asaf Noy Lihi Zelnik-Manor SSeg VLM CLIP 154 676 0 22 Apr 2021
SWAD: Domain Generalization by Seeking Flat Minima Junbum Cha Sanghyuk Chun Kyungjae Lee Han-Cheol Cho Seunghyun Park Yunsung Lee Sungrae Park MoMe 216 422 0 17 Feb 2021
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 237 11,568 0 09 Mar 2017