Can Pre-trained Networks Detect Familiar Out-of-Distribution Data?

2 October 2023

Papers citing "Can Pre-trained Networks Detect Familiar Out-of-Distribution Data?"

10 / 10 papers shown

Title
Is Fine-tuning Needed? Pre-trained Language Models Are Near Perfect for Out-of-Domain Detection Rheeya Uppaal Junjie Hu Yixuan Li OODD 101 22 0 22 May 2023
LINe: Out-of-Distribution Detection by Leveraging Important Neurons Yong Hyun Ahn Gyeong-Moon Park Seong Tae Kim OODD 97 20 0 24 Mar 2023
Masked Autoencoders Are Scalable Vision Learners Kaiming He Xinlei Chen Saining Xie Yanghao Li Piotr Dollár Ross B. Girshick ViT TPM 249 5,353 0 11 Nov 2021
Generalized Out-of-Distribution Detection: A Survey Jingkang Yang Kaiyang Zhou Yixuan Li Ziwei Liu 147 812 0 21 Oct 2021
Open-Set Recognition: a Good Closed-Set Classifier is All You Need? S. Vaze Kai Han Andrea Vedaldi Andrew Zisserman BDL 149 292 0 12 Oct 2021
ResNet strikes back: An improved training procedure in timm Ross Wightman Hugo Touvron Hervé Jégou AI4TS 194 403 0 01 Oct 2021
On the Importance of Gradients for Detecting Distributional Shifts in the Wild Rui Huang Andrew Geng Yixuan Li 162 259 0 01 Oct 2021
Emerging Properties in Self-Supervised Vision Transformers Mathilde Caron Hugo Touvron Ishan Misra Hervé Jégou Julien Mairal Piotr Bojanowski Armand Joulin 277 4,299 0 29 Apr 2021
ImageNet-21K Pretraining for the Masses T. Ridnik Emanuel Ben-Baruch Asaf Noy Lihi Zelnik-Manor SSeg VLM CLIP 145 542 0 22 Apr 2021
Improved Baselines with Momentum Contrastive Learning Xinlei Chen Haoqi Fan Ross B. Girshick Kaiming He SSL 224 3,029 0 09 Mar 2020