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As diffusion models are deployed in real-world settings, and their performance is driven by training data, appraising the contribution of data contributors is crucial to creating incentives for sharing quality data and to implementing policies for data compensation.
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Jonathan Frankle and Michael Carbin · 2018
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Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2018
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Input similarity from the neural network perspective
Guillaume Charpiat, Nicolas Girard, Loris Felardos, and Yuliya Tarabalka · 2019
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Centripetal sgd for pruning very deep convolutional networks with complicated structure
Xiaohan Ding, Guiguang Ding, Yuchen Guo, and Jungong Han · 2019
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Amirata Ghorbani and James Zou · 2019
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A benchmark for interpretability methods in deep neural networks
Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans, and Been Kim · 2019
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On the accuracy of influence functions for measuring group effects
Pang Wei W Koh, Kai-Siang Ang, Hubert Teo, and Percy S Liang · 2019
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Ali Razavi, Aaron Van den Oord, and Oriol Vinyals · 2019
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Hierarchical text-conditional image generation with clip latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
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Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Laion-5b: An open large-scale dataset for training next generation image-text models
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Zheng Dai and David K Gifford · 2023
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Gongfan Fang, Xinyin Ma, and Xinchao Wang · 2023
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America already has an ai underclass
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