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An image classifier may depend on incidental features stemming from a strong correlation between the feature and the classification target in the training dataset.
Multivariate regression model with constraints
Lubomír Kubáček · 2007
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Im2text: Describing images using 1 million captioned photographs
Vicente Ordonez, Girish Kulkarni, and Tamara Berg · 2011
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The caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
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Microsoft coco captions: Data collection and evaluation server
Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollár, and C Lawrence Zitnick · 2015
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Places: A 10 million image database for scene recognition
Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
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Umap: Uniform manifold approximation and projection for dimension reduction
Leland McInnes, John Healy, and James Melville · 2018
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Learning to interpret satellite images in global scale using wikipedia
Burak Uzkent, Evan Sheehan, Chenlin Meng, Zhongyi Tang, Marshall Burke, David Lobell, and Stefano Ermon · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Learning from failure: De-biasing classifier from biased classifier
Junhyun Nam, Hyuntak Cha, Sungsoo Ahn, Jaeho Lee, and Jinwoo Shin · 2020
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Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization
Shiori Sagawa, Pang Wei Koh, Tatsunori B Hashimoto, and Percy Liang · 2020
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No subclass left behind: Fine-grained robustness in coarse-grained classification problems
Nimit Sohoni, Jared Dunnmon, Geoffrey Angus, Albert Gu, and Christopher Ré · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2021
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Scaling up visual and vision-language representation learning with noisy text supervision
Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig · 2021
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Scaling up visual and vision-language representation learning with noisy text supervision
Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig · 2021
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Just train twice: Improving group robustness without training group information
Evan Z Liu, Behzad Haghgoo, Annie S Chen, Aditi Raghunathan, Pang Wei Koh, Shiori Sagawa, Percy Liang, and Chelsea Finn · 2021
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Spread spurious attribute: Improving worst-group accuracy with spurious attribute estimation
Junhyun Nam, Jaehyung Kim, Jaeho Lee, and Jinwoo Shin · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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The spotlight: A general method for discovering systematic errors in deep learning models
Greg d’Eon, Jason d’Eon, James R Wright, and Kevin Leyton-Brown · 2022
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Domino: Discovering systematic errors with cross-modal embeddings
Sabri Eyuboglu, Maya Varma, Khaled Saab, Jean-Benoit Delbrouck, Christopher Lee-Messer, Jared Dunnmon, James Zou, and Christopher Ré · 2022
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I can’t believe there’s no images! learning visual tasks using only language supervision
Sophia Gu, Christopher Clark, and Aniruddha Kembhavi · 2023
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Lei Huang, Weijiang Yu, Weitao Ma, Weihong Zhong, Zhangyin Feng, Haotian Wang, Qianglong Chen, Weihua Peng, Xiaocheng Feng, Bing Qin, and Ting Liu · 2023
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Towards mitigating spurious correlations in the wild: A benchmark & a more realistic dataset
Siddharth Joshi, Yu Yang, Yihao Xue, Wenhan Yang, and Baharan Mirzasoleiman · 2023
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Grounding counterfactual explanation of image classifiers to textual concept space
Siwon Kim, Jinoh Oh, Sungjin Lee, Seunghak Yu, Jaeyoung Do, and Tara Taghavi · 2023
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Towards last-layer retraining for group robustness with fewer annotations
Tyler LaBonte, Vidya Muthukumar, and Abhishek Kumar · 2023
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Simple data balancing achieves competitive worst-group-accuracy
Badr Youbi Idrissi, Martin Arjovsky, Mohammad Pezeshki, and David Lopez-Paz · 2022
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Last layer re-training is sufficient for robustness to spurious correlations
Polina Kirichenko, Pavel Izmailov, and Andrew Gordon Wilson · 2022
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Mind the gap: Understanding the modality gap in multi-modal contrastive representation learning
Victor Weixin Liang, Yuhui Zhang, Yongchan Kwon, Serena Yeung, and James Y Zou · 2022
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Grounding visual representations with texts for domain generalization
Seonwoo Min, Nokyung Parpk, Siwon Kim, Seunghyun Park, and Jinkyu Kim · 2022
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Seal: Interactive tool for systematic error analysis and labeling
Nazneen Rajani, Weixin Liang, Lingjiao Chen, Meg Mitchell, and James Zou · 2022
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Laion-5b: An open large-scale dataset for training next generation image-text models
Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al · 2022
Cited alongside, same era.
Jianhao Yuan, Francesco Pinto, Adam Davies, Aarushi Gupta, and Philip Torr · 2022
Cited alongside, same era.
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Decap: Decoding clip latents for zero-shot captioning via text-only training
Wei Li, Linchao Zhu, Longyin Wen, and Yi Yang · 2023
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Text-to-concept (and back) via cross-model alignment
Mazda Moayeri, Keivan Rezaei, Maziar Sanjabi, and Soheil Feizi · 2023
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Simple and fast group robustness by automatic feature reweighting
Shikai Qiu, Andres Potapczynski, Pavel Izmailov, and Andrew Gordon Wilson · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
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Effective data augmentation with diffusion models
Brandon Trabucco, Kyle Doherty, Max Gurinas, and Ruslan Salakhutdinov · 2023
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Textmania: Enriching visual feature by text-driven manifold augmentation
Moon Ye-Bin, Jisoo Kim, Hongyeob Kim, Kilho Son, and Tae-Hyun Oh · 2023
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Diffusion models and semi-supervised learners benefit mutually with few labels
Zebin You, Yong Zhong, Fan Bao, Jiacheng Sun, Chongxuan Li, and Jun Zhu · 2023
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Siren’s song in the ai ocean: A survey on hallucination in large language models
Yue Zhang, Yafu Li, Leyang Cui, Deng Cai, Lemao Liu, Tingchen Fu, Xinting Huang, Enbo Zhao, Yu Zhang, Yulong Chen, et al · 2023
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Lovm: Language-only vision model selection
Orr Zohar, Shih-Cheng Huang, Kuan-Chieh Wang, and Serena Yeung · 2023
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Fairerclip: Debiasing zero-shot predictions of clip in rkhss
Sepehr Dehdashtian, Lan Wang, and Vishnu Naresh Boddeti · 2024
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From fake to real (ffr): A two-stage training pipeline for mitigating spurious correlations with synthetic data
Maan Qraitem, Kate Saenko, and Bryan A Plummer · 2024
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A survey on large language model (llm) security and privacy: The good, the bad, and the ugly
Yifan Yao, Jinhao Duan, Kaidi Xu, Yuanfang Cai, Zhibo Sun, and Yue Zhang · 2024
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