Coarse-to-Fine Concept Bottleneck Models

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.

Sun database: Large-scale scene recognition from abbey to zoo

Jianxiong Xiao, James Hays, Krista A. Ehinger, Aude Oliva, and Antonio Torralba · 2010

Earlier work this paper cites.

A joint learning framework for attribute models and object descriptions

Dhruv Mahajan, Sundararajan Sellamanickam, and Vinod Nair · 2011

Earlier work this paper cites.

The caltech-ucsd birds-200-2011 dataset

C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011

Earlier work this paper cites.

Stochastic variational inference

Matthew D. Hoffman, David M. Blei, Chong Wang, and John Paisley · 2013

Earlier work this paper cites.

Auto-encoding variational bayes

Diederik P. Kingma and Max Welling · 2014

Earlier work this paper cites.

Improved deep metric learning with multi-class n-pair loss objective

Kihyuk Sohn · 2016

Earlier work this paper cites.

Network dissection: Quantifying interpretability of deep visual representations

David Bau, Bolei Zhou, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017

Earlier work this paper cites.

beta-VAE: Learning basic visual concepts with a constrained variational framework

Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017

Cited alongside, same era.

Categorical reparametrization with gumbel-softmax

Eric Jang, Shixiang Gu, and Ben Poole · 2017

Cited alongside, same era.

The concrete distribution: A continuous relaxation of discrete random variables

Chris J. Maddison, Andriy Mnih, and Yee Whye Teh · 2017

Cited alongside, same era.

A simple framework for contrastive learning of visual representations

Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton · 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.

Contextual semantic interpretability

Diego Marcos, Ruth Fong, Sylvain Lobry, Rémi Flamary, Nicolas Courty, and Devis Tuia · 2020

Post-hoc concept bottleneck models

Mert Yuksekgonul, Maggie Wang, and James Zou · 2022

Later among the works it cites.

Regionclip: Region-based language-image pretraining

Yiwu Zhong, Jianwei Yang, Pengchuan Zhang, Chunyuan Li, Noel Codella, Liunian Harold Li, Luowei Zhou, Xiyang Dai, Lu Yuan, Yin Li, et al · 2022

Later among the works it cites.

Probabilistic concept bottleneck models

Eunji Kim, Dahuin Jung, Sangha Park, Siwon Kim, and Sungroh Yoon · 2023

Closest in time.

Label-free concept bottleneck models

Tuomas Oikarinen, Subhro Das, Lam M. Nguyen, and Tsui-Wei Weng · 2023

Closest in time.

CLIP-dissect: Automatic description of neuron representations in deep vision networks

Tuomas Oikarinen and Tsui-Wei Weng · 2023

Closest in time.

Sparse linear concept discovery models

Konstantinos P. Panousis, Dino Ienco, and Diego Marcos · 2023

Closest in time.