Data-Centric Debugging: mitigating model failures via targeted data collection

17 November 2022

Papers citing "Data-Centric Debugging: mitigating model failures via targeted data collection"

8 / 8 papers shown

Title
Evaluating Model Bias Requires Characterizing its Mistakes Isabela Albuquerque Jessica Schrouff David Warde-Farley Ali Taylan Cemgil Sven Gowal Olivia Wiles 47 2 0 15 Jul 2024
Identifying and Mitigating Model Failures through Few-shot CLIP-aided Diffusion Generation Atoosa Malemir Chegini S. Feizi VLM 40 4 0 09 Dec 2023
LANCE: Stress-testing Visual Models by Generating Language-guided Counterfactual Images Viraj Prabhu Sriram Yenamandra Prithvijit Chattopadhyay Judy Hoffman 22 37 0 30 May 2023
Do Deep Neural Networks Always Perform Better When Eating More Data? Jiachen Yang Zhuo Zhang Yicheng Gong Shukun Ma Xiaolan Guo ... Jiabao Wen Yang Li Xin Gao Wen Lu Qinggang Meng OOD 16 5 0 30 May 2022
Editing a classifier by rewriting its prediction rules Shibani Santurkar Dimitris Tsipras Mahalaxmi Elango David Bau Antonio Torralba A. Madry KELM 180 89 0 02 Dec 2021
Masked Autoencoders Are Scalable Vision Learners Kaiming He Xinlei Chen Saining Xie Yanghao Li Piotr Dollár Ross B. Girshick ViT TPM 305 7,443 0 11 Nov 2021
Fast Model Editing at Scale E. Mitchell Charles Lin Antoine Bosselut Chelsea Finn Christopher D. Manning KELM 230 343 0 21 Oct 2021
Emerging Properties in Self-Supervised Vision Transformers Mathilde Caron Hugo Touvron Ishan Misra Hervé Jégou Julien Mairal Piotr Bojanowski Armand Joulin 317 5,785 0 29 Apr 2021