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Interpretable computer vision models explain their classifications through comparing the distances between the local embeddings of an image and a set of prototypes that represent the training data.
New methods for the initialisation of clusters
Moh’d B Al-Daoud and Stuart A Roberts · 1996
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Bayesian modelling and inference on mixtures of distributions
Jean-Michel Marin, Kerrie Mengersen, and Christian P Robert · 2005
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Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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Data clustering: 50 years beyond k-means
Anil K Jain · 2010
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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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Cats and dogs
Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, and CV Jawahar · 2012
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3D object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
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Fine-grained visual classification of aircraft
Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi · 2013
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On the difficulty of training recurrent neural networks
Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio · 2013
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Food-101–mining discriminative components with random forests
Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool · 2014
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ImageNet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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Learning deep features for discriminative localization
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba · 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Directional statistics in machine learning: A brief review
Suvrit Sra · 2018
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This looks like that: Deep learning for interpretable image recognition
Chaofan Chen, Oscar Li, Daniel Tao, Alina Barnett, Cynthia Rudin, and Jonathan K Su · 2019
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A closer look at deep learning heuristics: Learning rate restarts, warmup and distillation
Akhilesh Gotmare, Nitish Shirish Keskar, Caiming Xiong, and Richard Socher · 2019
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Do ImageNet classifiers generalize to ImageNet?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 2019
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin · 2019
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Learning robust global representations by penalizing local predictive power
Haohan Wang, Songwei Ge, Zachary Lipton, and Eric P Xing · 2019
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End-to-end object detection with transformers
Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko · 2020
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Bootstrap your own latent: A new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al · 2020
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Early identification of an impending rockslide location via a spatially-aided Gaussian mixture model
Shuo Zhou, Howard Bondell, Antoinette Tordesillas, Benjamin I. P. Rubinstein, and James Bailey · 2020
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Semi-supervised learning of visual features by non-parametrically predicting view assignments with support samples
Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Armand Joulin, Nicolas Ballas, and Michael Rabbat · 2021
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Emerging properties in self-supervised Vision Transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
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Intriguing properties of contrastive losses
Ting Chen, Calvin Luo, and Lala Li · 2021
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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, Jakob Uszkoreit, and Neil Houlsby · 2021
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Funnybirds: A synthetic vision dataset for a part-based analysis of explainable AI methods
Robin Hesse, Simone Schaub-Meyer, and Stefan Roth · 2023
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Minimalist and high-performance semantic segmentation with plain Vision Transformers
Yuanduo Hong, Jue Wang, Weichao Sun, and Huihui Pan · 2023
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Evaluation and improvement of interpretability for self-explainable part-prototype networks
Qihan Huang, Mengqi Xue, Wenqi Huang, Haofei Zhang, Jie Song, Yongcheng Jing, and Mingli Song · 2023
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End-to-end deep learning of lane detection and path prediction for real-time autonomous driving
Der-Hau Lee and Jinn-Liang Liu · 2023
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Visual instruction tuning
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee · 2023
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Adrian Hoffmann, Claudio Fanconi, Rahul Rade, and Jonas Kohler · 2021
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All tokens matter: Token labeling for training better Vision Transformers
Zi-Hang Jiang, Qibin Hou, Li Yuan, Daquan Zhou, Yujun Shi, Xiaojie Jin, Anran Wang, and Jiashi Feng · 2021
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Neural prototype trees for interpretable fine-grained image recognition
Meike Nauta, Ron Van Bree, and Christin Seifert · 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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ProtoPShare: Prototypical parts sharing for similarity discovery in interpretable image classification
Dawid Rymarczyk, Łukasz Struski, Jacek Tabor, and Bartosz Zieliński · 2021
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Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2021
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Interpretable image recognition by constructing transparent embedding space
Jiaqi Wang, Huafeng Liu, Xinyue Wang, and Liping Jing · 2021
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Evelyn Mannix and Howard Bondell · 2023
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RoPAWS: Robust semi-supervised representation learning from uncurated data
Sangwoo Mo, Jong-Chyi Su, Chih-Yao Ma, Mido Assran, Ishan Misra, Licheng Yu, and Sean Bell · 2023
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PIP-Net: Patch-based intuitive prototypes for interpretable image classification
Meike Nauta, Jörg Schlötterer, Maurice van Keulen, and Christin Seifert · 2023
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Improving visual representation learning through perceptual understanding
Samyakh Tukra, Frederick Hoffman, and Ken Chatfield · 2023
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Fully attentional networks with self-emerging token labeling
Bingyin Zhao, Zhiding Yu, Shiyi Lan, Yutao Cheng, Anima Anandkumar, Yingjie Lao, and Jose M Alvarez · 2023
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Deep learning for medical image analysis
S Kevin Zhou, Hayit Greenspan, and Dinggang Shen · 2023
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DINOv2 based self supervised learning for few shot medical image segmentation
Lev Ayzenberg, Raja Giryes, and Hayit Greenspan · 2024
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Interpreting CLIP with sparse linear concept embeddings (spliCE)
Usha Bhalla, Alex Oesterling, Suraj Srinivas, Flavio Calmon, and Himabindu Lakkaraju · 2024
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Vision Transformers need registers
Timothée Darcet, Maxime Oquab, Julien Mairal, and Piotr Bojanowski · 2024
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Amortized variational inference: when and why?
Charles C Margossian and David M Blei · 2024
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DINOv2: Learning robust visual features without supervision
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy V. Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel HAZIZA, Francisco Massa, Alaaeldin El-Nouby, Mido Assran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael Rabbat, Vasu Sharma, Gabriel Synnaeve, Hu Xu, Herve Jegou, Julien Mairal, Patrick Labatut, Armand Joulin, and Piotr Bojanowski · 2024
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A simple interpretable transformer for fine-grained image classification and analysis
Dipanjyoti Paul, Arpita Chowdhury, Xinqi Xiong, Feng-Ju Chang, David Edward Carlyn, Samuel Stevens, Kaiya L Provost, Anuj Karpatne, Bryan Carstens, Daniel Rubenstein, Charles Stewart, Tanya Berger-Wolf, Yu Su, and Wei-Lun Chao · 2024
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ProtoPFormer: Concentrating on prototypical parts in Vision Transformers for interpretable image recognition
Mengqi Xue, Qihan Huang, Haofei Zhang, Jingwen Hu, Jie Song, Mingli Song, and Canghong Jin · 2024
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Low-resource vision challenges for foundation models
Yunhua Zhang, Hazel Doughty, and Cees GM Snoek · 2024
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Michael Tschannen, Alexey Gritsenko, Xiao Wang, Muhammad Ferjad Naeem, Ibrahim Alabdulmohsin, Nikhil Parthasarathy, Talfan Evans, Lucas Beyer, Ye Xia, Basil Mustafa, et al · 2025
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Interpretable image classification via non-parametric part prototype learning
Zhijie Zhu, Lei Fan, Maurice Pagnucco, and Yang Song · 2025
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