mlsanityData to 7 Oct 2026

Additive Class Distinction Maps using Branched-GANs

arXiv 2305.02899 ↗First posted 4 May 2023cs.CVcited by 0

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Abstract (arXiv)

We present a new model, training procedure and architecture to create precise maps of distinction between two classes of images. The objective is to comprehend, in pixel-wise resolution, the unique characteristics of a class. These maps can facilitate self-supervised segmentation and objectdetection in addition to new capabilities in explainable AI (XAI). Our proposed architecture is based on image decomposition, where the output is the sum of multiple generative networks (branched-GANs). The distinction between classes is isolated in a dedicated branch. This approach allows clear, precise and interpretable visualization of the unique characteristics of each class. We show how our generic method can be used in several modalities for various tasks, such as MRI brain tumor extraction, isolating cars in aerial photography and obtaining feminine and masculine face features. This is a preliminary report of our initial findings and results.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 24 of its 36 references that are in the map. First posted on arXiv 4 May 2023.

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24 of 36 references.

References · 24

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12 more
  1. 2022A Review of Sparse Expert Models in Deep Learningcited by 147
  2. 2018Adversarially Learned Anomaly Detectioncited by 115
  3. 2018CT Super-resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble(GAN-CIRCLE)cited by 81
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  5. 2022Towards Understanding Mixture of Experts in Deep Learningcited by 52
  6. 2020MADGAN: unsupervised Medical Anomaly Detection GAN using multiple adjacent brain MRI slice reconstructioncited by 50
  7. 2020Deep Interpretable Classification and Weakly-Supervised Segmentation of Histology Images via Max-Min Uncertaintycited by 26
  8. 2020GAN Ensemble for Anomaly Detectioncited by 22
  9. 2020High Tissue Contrast MRI Synthesis Using Multi-Stage Attention-GAN for Glioma Segmentationcited by 10
  10. 2021Unsupervised Detection of Cancerous Regions in Histology Imagery using Image-to-Image Translationcited by 4
  11. 2022Weakly-supervised segmentation using inherently-explainable classification models and their application to brain tumour classificationcited by 3
  12. 2022Analysis of Branch Specialization and its Application in Image Decompositioncited by 2

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