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20222025
most citedCombining Counterfactuals With Shapley Values To Explain Image Models

7 citations · 11 across the 8 of their papers we have counts for

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5 papers · 1 filter

cs.CV2025

Spherical Leech Quantization for Visual Tokenization and Generation

Yue Zhao, Hanwen Jiang, Zhenlin Xu +3

Non-parametric quantization has received much attention due to its efficiency on parameters and scalability to a large codebook. In this paper, we present a unified formulation of…

cs.CV2025

Artist-Created Mesh Generation from Raw Observation

Yao He, Youngjoong Kwon, Wenxiao Cai +1

We present an end-to-end framework for generating artist-style meshes from noisy or incomplete point clouds, such as those captured by real-world sensors like LiDAR or mobile RGB-D…

cs.CV2024

SOE: SO(3)-Equivariant 3D MRI Encoding

Shizhe He, Magdalini Paschali, Jiahong Ouyang +3

Representation learning has become increasingly important, especially as powerful models have shifted towards learning latent representations before fine-tuning for downstream task…

cs.CV2024

Enforcing Conditional Independence for Fair Representation Learning and Causal Image Generation

Jensen Hwa, Qingyu Zhao, Aditya Lahiri +3

Conditional independence (CI) constraints are critical for defining and evaluating fairness in machine learning, as well as for learning unconfounded or causal representations. Tra…

cs.CV2022★ 4 cited

Explaining Image Classifiers Using Contrastive Counterfactuals in Generative Latent Spaces

Kamran Alipour, Aditya Lahiri, Ehsan Adeli +2

Despite their high accuracies, modern complex image classifiers cannot be trusted for sensitive tasks due to their unknown decision-making process and potential biases. Counterfact…