adaptation. In International Conference on Learning Representations (ICLR), 2022. https://arxiv.org/abs/2112.05090.
[128] Christoph Schuhmann, Richard Vencu, Romain Beaumont, Robert Kaczmarczyk, Clayton Mullis, Aarush Katta, Theo Coombes, Jenia Jitsev, and Aran Komatsuzaki. LAION-400M: Open dataset of clip-filtered 400 million image-text pairs, 2021. https://arxiv.org/abs/2111.02114.
[129] Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade W Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, Patrick Schramowski, Srivatsa R Kundurthy, Katherine Crowson, Ludwig Schmidt, Robert Kaczmarczyk, and Jenia Jitsev. LAION-5B: An open large-scale dataset for training next generation image-text models. In Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track, 2022. https://openreview.net/forum?id=M3Y74vmsMcY.
[130] Ozan Sener and Silvio Savarese. Active learning for convolutional neural networks: A core-set approach. In International Conference on Learning Representations (ICLR), 2018. https://openreview.net/forum?id=H1aIuk-RW.
[131] Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut. Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning. In Annual Meeting of the Association for Computational Linguistics (ACL), 2018. https://aclanthology.org/P18-1238/.
[132] Sheng Shen, Liunian Harold Li, Hao Tan, Mohit Bansal, Anna Rohrbach, Kai-Wei Chang, Zhewei Yao, and Kurt Keutzer. How much can clip benefit vision-and-language tasks?, 2021. https://arxiv.org/abs/2107.06383.
[133] Changho Shin, Winfred Li, Harit Vishwakarma, Nicholas Roberts, and Frederic Sala. Universalizing weak supervision. In International Conference on Learning Representations (ICLR), 2022. https://openreview.net/forum?id=YpPiNigTzMT.
[134] Haoyu Song, Li Dong, Weinan Zhang, Ting Liu, and Furu Wei. CLIP models are few-shot learners: Empirical studies on VQA and visual entailment. In Annual Meeting of the Association for Computational Linguistics (ACL), 2022. https://aclanthology.org/2022.acl-long.421.
[135] Ben Sorscher, Robert Geirhos, Shashank Shekhar, Surya Ganguli, and Ari S. Morcos. Beyond neural scaling laws: beating power law scaling via data pruning. In Advances in Neural Information Processing Systems (NeurIPS), 2022. https://openreview.net/forum?id=UmvSlP-PyV.
[136] Krishna Srinivasan, Karthik Raman, Jiecao Chen, Michael Bendersky, and Marc Najork. Wit: Wikipedia-based image text dataset for multimodal multilingual machine learning. In 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021. https://arxiv.org/abs/2103.01913.
[137] Johannes Stallkamp, Marc Schlipsing, Jan Salmen, and Christian Igel. The german traffic sign recognition benchmark: a multi-class classification competition. In International Joint Conference on Neural Networks (IJCNN), 2011. https://ieeexplore.ieee.org/document/6033395.
[138] Swabha Swayamdipta, Roy Schwartz, Nicholas Lourie, Yizhong Wang, Hannaneh Hajishirzi, Noah A. Smith, and Yejin Choi. Dataset cartography: Mapping and diagnosing datasets with training dynamics. In Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020. https://aclanthology.org/2020.emnlp-main.746.
[139] Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht, and Ludwig Schmidt. Measuring robustness to natural distribution shifts in image classification. In Advances in Neural Information Processing Systems (NeurIPS), 2020. https://dl.acm.org/doi/abs/10.5555/3495724.3497285.
[140] Bart Thomee, David A Shamma, Gerald Friedland, Benjamin Elizalde, Karl Ni, Douglas Poland, Damian Borth, and Li-Jia Li. YFCC100M: The new data in multimedia research. Communications of the ACM, 2016. https://arxiv.org/abs/1503.01817.