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CLIP embeddings have demonstrated remarkable performance across a wide range of multimodal applications.
Independent component analysis, a new concept?
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Sparse coding with an overcomplete basis set: A strategy employed by v1?
Bruno A Olshausen and David J Field · 1997
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Aapo Hyvärinen and Erkki Oja · 2000
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Prototypicality, distinctiveness, and intercorrelation: Analyses of the semantic attributes of living and nonliving concepts
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Semantic feature production norms for a large set of living and nonliving things
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The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results
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Semantic feature production norms for a large set of objects and events
David P Vinson and Gabriella Vigliocco · 2008
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Learning to detect unseen object classes by between-class attribute transfer
Christoph H Lampert, Hannes Nickisch, and Stefan Harmeling · 2009
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Attribute and simile classifiers for face verification
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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A fast iterative shrinkage-thresholding algorithm for linear inverse problems
Amir Beck and Marc Teboulle · 2009
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Efficient object category recognition using classemes
Lorenzo Torresani, Martin Szummer, and Andrew Fitzgibbon · 2010
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Sun database: Large-scale scene recognition from abbey to zoo
Jianxiong Xiao, James Hays, Krista A Ehinger, Aude Oliva, and Antonio Torralba · 2010
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Scikit-learn: Machine learning in Python
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Distributed optimization and statistical learning via the alternating direction method of multipliers
Stephen Boyd, Neal Parikh, Eric Chu, Borja Peleato, Jonathan Eckstein, et al · 2011
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An analysis of single-layer networks in unsupervised feature learning
Adam Coates, Andrew Ng, and Honglak Lee · 2011
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Fine-grained categorization for 3d scene understanding
Michael Stark, Jonathan Krause, Bojan Pepik, David Meger, James J Little, Bernt Schiele, and Daphne Koller · 2011
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Learning effective and interpretable semantic models using non-negative sparse embedding
Brian Murphy, Partha Talukdar, and Tom Mitchell · 2012
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Deep learning of representations: Looking forward
Yoshua Bengio · 2013
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Linguistic regularities in continuous space word representations
Tomáš Mikolov, Wen-tau Yih, and Geoffrey Zweig · 2013
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Interpretable semantic vectors from a joint model of brain-and text-based meaning
Alona Fyshe, Partha P Talukdar, Brian Murphy, and Tom M Mitchell · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Sparse overcomplete word vector representations
Manaal Faruqui, Yulia Tsvetkov, Dani Yogatama, Chris Dyer, and Noah Smith · 2015
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A compositional and interpretable semantic space
Alona Fyshe, Leila Wehbe, Partha Talukdar, Brian Murphy, and Tom Mitchell · 2015
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Laion-400m: Open dataset of clip-filtered 400 million image-text pairs
Christoph Schuhmann, Richard Vencu, Romain Beaumont, Robert Kaczmarczyk, Clayton Mullis, Aarush Katta, Theo Coombes, Jenia Jitsev, and Aran Komatsuzaki · 2021
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Openclip
Gabriel Ilharco, Mitchell Wortsman, Ross Wightman, Cade Gordon, Nicholas Carlini, Rohan Taori, Achal Dave, Vaishaal Shankar, Hongseok Namkoong, John Miller, Hannaneh Hajishirzi, Ali Farhadi, and Ludwig Schmidt · 2021
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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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Linearly mapping from image to text space
Jack Merullo, Louis Castricato, Carsten Eickhoff, and Ellie Pavlick · 2022
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Acquisition of chess knowledge in alphazero
Thomas McGrath, Andrei Kapishnikov, Nenad Tomašev, Adam Pearce, Martin Wattenberg, Demis Hassabis, Been Kim, Ulrich Paquet, and Vladimir Kramnik · 2022
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Phillip Isola, Joseph J. Lim, and Edward H. Adelson · 2015
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
Cited alongside, same era.
A latent variable model approach to pmi-based word embeddings
Sanjeev Arora, Yuanzhi Li, Yingyu Liang, Tengyu Ma, and Andrej Risteski · 2016
Cited alongside, same era.
" why should i trust you?" explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Cited alongside, same era.
Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
Cited alongside, same era.
Feature visualization
Chris Olah, Alexander Mordvintsev, and Ludwig Schubert · 2017
Cited alongside, same era.
Network dissection: Quantifying interpretability of deep visual representations
David Bau, Bolei Zhou, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
Cited alongside, same era.
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Do vision-language pretrained models learn composable primitive concepts?
Tian Yun, Usha Bhalla, Ellie Pavlick, and Chen Sun · 2022
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Bertopic: Neural topic modeling with a class-based tf-idf procedure
Maarten Grootendorst · 2022
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Vikram V Ramaswamy, Sunnie SY Kim, Ruth Fong, and Olga Russakovsky · 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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Disentanglement via latent quantization
Kyle Hsu, Will Dorrell, James CR Whittington, Jiajun Wu, and Chelsea Finn · 2023
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Don’t trust your eyes: on the (un) reliability of feature visualizations
Robert Geirhos, Roland S Zimmermann, Blair Bilodeau, Wieland Brendel, and Been Kim · 2023
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The linear representation hypothesis and the geometry of large language models
Kiho Park, Yo Joong Choe, and Victor Veitch · 2023
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Dear: Debiasing vision-language models with additive residuals
Ashish Seth, Mayur Hemani, and Chirag Agarwal · 2023
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Information maximization perspective of orthogonal matching pursuit with applications to explainable ai
Aditya Chattopadhyay, Ryan Pilgrim, and Rene Vidal · 2023
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Label-free concept bottleneck models
Tuomas Oikarinen, Subhro Das, Lam M Nguyen, and Tsui-Wei Weng · 2023
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Sparse linear concept discovery models
Konstantinos Panagiotis Panousis, Dino Ienco, and Diego Marcos · 2023
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A holistic approach to unifying automatic concept extraction and concept importance estimation
Thomas Fel, Victor Boutin, Mazda Moayeri, Rémi Cadène, Louis Bethune, Mathieu Chalvidal, Thomas Serre, et al · 2023
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Towards monosemanticity: Decomposing language models with dictionary learning
Trenton Bricken, Adly Templeton, Joshua Batson, Brian Chen, Adam Jermyn, Tom Conerly, Nick Turner, Cem Anil, Carson Denison, Amanda Askell, et al · 2023
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Aleksandar Makelov, Georg Lange, and Neel Nanda · 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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Interpreting clip’s image representation via text-based decomposition
Yossi Gandelsman, Alexei A Efros, and Jacob Steinhardt · 2023
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Discover and cure: Concept-aware mitigation of spurious correlation
Shirley Wu, Mert Yuksekgonul, Linjun Zhang, and James Zou · 2023
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