Fetching the paper…
Reading the bibliography…
In this paper, we introduce an explainable algorithm designed from a multi-modal foundation model, that performs fast and explainable image classification.
Induction of decision trees
J. Ross Quinlan · 1986
Earlier work this paper cites.
Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
The Elements of Statistical Learning: Data Mining, Inference, and Prediction , volume 2
Trevor Hastie, Robert Tibshirani, and Jerome H. Friedman · 2009
Earlier work this paper cites.
Attribute and simile classifiers for face verification
Neeraj Kumar, Alexander C. Berg, Peter N. Belhumeur, and Shree K. Nayar · 2009
Earlier work this paper cites.
Learning to detect unseen object classes by between-class attribute transfer
Christoph H. Lampert, Hannes Nickisch, and Stefan Harmeling · 2009
Earlier work this paper cites.
Recognizing indoor scenes
Ariadna Quattoni and Antonio Torralba · 2009
Earlier work this paper cites.
Opening black box data mining models using sensitivity analysis
Paulo Cortez and Mark J. Embrechts · 2011
Earlier work this paper cites.
Dogs vs. cats, 2013
Will Cukierski · 2013
Earlier work this paper cites.
3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
Earlier work this paper cites.
Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
Earlier work this paper cites.
Integration of numeric and symbolic information for semantic image interpretation
Ivan Donadello and Luciano Serafini · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Perceptual losses for real-time style transfer and super-resolution
Justin Johnson, Alexandre Alahi, and Li Fei-Fei · 2016
Earlier work this paper cites.
“Why should I trust you?”: Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Earlier work this paper cites.
A unified approach to interpreting model predictions
Scott M. Lundberg and Su-In Lee · 2017
Earlier work this paper cites.
Elements of Causal Inference: Foundations and Learning Algorithms
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2017
Earlier work this paper cites.
On wasserstein two-sample testing and related families of nonparametric tests
Aaditya Ramdas, Nicolás García Trillos, and Marco Cuturi · 2017
Earlier work this paper cites.
Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
Cited alongside, same era.
A deeper look at dataset bias
Tatiana Tommasi, Novi Patricia, Barbara Caputo, and Tinne Tuytelaars · 2017
Cited alongside, same era.
Graphical Methods for Data Analysis
John M. Chambers · 2018
Cited alongside, same era.
On the generalized distance in statistics
Prasanta Chandra Mahalanobis · 2018
Cited alongside, same era.
RISE: Randomized input sampling for explanation of black-box models
Vitali Petsiuk, Abir Das, and Kate Saenko · 2018
Cited alongside, same era.
Model agnostic supervised local explanations
Gregory Plumb, Denali Molitor, and Ameet S Talwalkar · 2018
Explainable neural-symbolic learning (x-nesyl) methodology to fuse deep learning representations with expert knowledge graphs: The monumai cultural heritage use case
Natalia Díaz-Rodríguez, Alberto Lamas, Jules Sanchez, Gianni Franchi, Ivan Donadello, Siham Tabik, David Filliat, Policarpo Cruz, Rosana Montes, and Francisco Herrera · 2022
Later among the works it cites.
Clip4clip: An empirical study of clip for end to end video clip retrieval and captioning
Huaishao Luo, Lei Ji, Ming Zhong, Yang Chen, Wen Lei, Nan Duan, and Tianrui Li · 2022
Later among the works it cites.
Visual classification via description from large language models
Sachit Menon and Carl Vondrick · 2022
Later among the works it cites.
Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
Later among the works it cites.
Finding and fixing spurious patterns with explanations
Gregory Plumb, Marco Tulio Ribeiro, and Ameet Talwalkar · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Interpretability beyond classification output: Semantic bottleneck networks
Max Losch, Mario Fritz, and Bernt Schiele · 2019
Cited alongside, same era.
Generalized Linear Models
Peter McCullagh · 2019
Cited alongside, same era.
Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador García, Sergio Gil-López, Daniel Molina, Richard Benjamins, et al · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Concept bottleneck models
Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, and Percy Liang · 2020
Cited alongside, same era.
Very deep transformers for neural machine translation
Xiaodong Liu, Kevin Duh, Liyuan Liu, and Jianfeng Gao · 2020
Cited alongside, same era.
A generalist agent
Scott Reed, Konrad Zolna, Emilio Parisotto, Sergio Gómez Colmenarejo, Alexander Novikov, Gabriel Barth-maron, Mai Giménez, Yury Sulsky, Jackie Kay, Jost Tobias Springenberg, et al · 2022
Later among the works it cites.
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
Later among the works it cites.
Post-hoc concept bottleneck models
Mert Yuksekgonul, Maggie Wang, and James Zou · 2022
Later among the works it cites.
Cem Akkus, Luyang Chu, Vladana Djakovic, Steffen Jauch-Walser, Philipp Koch, Giacomo Loss, Christopher Marquardt, Marco Moldovan, Nadja Sauter, Maximilian Schneider, et al · 2023
Closest in time.
Leveraging multiple descriptive features for robust few-shot image learning
Zhili Feng, Anna Bair, and J. Zico Kolter · 2023
Closest in time.
Wildclip: Scene and animal attribute retrieval from camera trap data with domain-adapted vision-language models
Valentin Gabeff, Marc Russwurm, Devis Tuia, and Alexander Mathis · 2023
Closest in time.
Quantus: An explainable ai toolkit for responsible evaluation of neural network explanations and beyond
Anna Hedström, Leander Weber, Daniel Krakowczyk, Dilyara Bareeva, Franz Motzkus, Wojciech Samek, Sebastian Lapuschkin, and Marina Marina M.-C. Höhne · 2023
Closest in time.
Grounding counterfactual explanation of image classifiers to textual concept space
Siwon Kim, Jinoh Oh, Sungjin Lee, Seunghak Yu, Jaeyoung Do, and Tara Taghavi · 2023
Closest in time.
Zero-shot model diagnosis
Jinqi Luo, Zhaoning Wang, Chen Henry Wu, Dong Huang, and Fernando De la Torre · 2023
Closest in time.
Label-free concept bottleneck models
Tuomas Oikarinen, Subhro Das, Lam Nguyen, and Lily Weng · 2023
Closest in time.
Sparse linear concept discovery models
Konstantinos P. Panousis, Dino Ienco, and Diego Marcos · 2023
Closest in time.
Language in a bottle: Language model guided concept bottlenecks for interpretable image classification
Yue Yang, Artemis Panagopoulou, Shenghao Zhou, Daniel Jin, Chris Callison-Burch, and Mark Yatskar · 2023
Closest in time.