InfoDisent: Explainability of Image Classification Models by Information Disentanglement
<|reference_start|>InfoDisent: Explainability of Image Classification Models by Information Disentanglement: Understanding the decisions made by image classification networks is a critical area of research in deep learning. This task is traditionally divided into two distinct approaches: post-hoc methods and intrinsic methods. Post-hoc methods, such as GradCam, aim to interpret the decisions of pre-trained models by identifying regions of the image where the network focuses its attention. However, these methods provide only a high-level overview, making it difficult to fully understand the network's decision-making process. Conversely, intrinsic methods, like prototypical parts models, offer a more detailed understanding of network predictions but are constrained by specific architectures, training methods, and datasets.   In this paper, we introduce InfoDisent, a hybrid model that combines the advantages of both approaches. By utilizing an information bottleneck, InfoDisent disentangles the information in the final layer of a pre-trained deep network, enabling the breakdown of classification decisions into basic, understandable atomic components. Unlike standard prototypical parts approaches, InfoDisent can interpret the decisions of pre-trained classification networks and be used for making classification decisions, similar to intrinsic models. We validate the effectiveness of InfoDisent on benchmark datasets such as ImageNet, CUB-200-2011, Stanford Cars, and Stanford Dogs for both convolutional and transformer backbones.<|reference_end|>
@article{struski2024infodisent:,
    title={InfoDisent: Explainability of Image Classification Models by Information
  Disentanglement},
    author={{L}ukasz Struski, Jacek Tabor},
    journal={arXiv preprint arXiv:2409.10329},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.10329},
    primaryClass={cs.CV cs.AI}
}