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This Explains That: Congruent Image-Report Generation for Explainable Medical Image Analysis with Cyclic Generative Adversarial Networks.

, , , , and . iMIMIC/TDA4MedicalData@MICCAI, volume 12929 of Lecture Notes in Computer Science, page 34-43. Springer, (2021)

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NGAME: Negative Mining-aware Mini-batching for Extreme Classification., , , , , , , , , and 9 other author(s). WSDM, page 258-266. ACM, (2023)Enhancing Tail Performance in Extreme Classifiers by Label Variance Reduction., , , , , , , , , and 3 other author(s). ICLR, OpenReview.net, (2024)Visual Representations of Physiological Signals for Fake Video Detection., , and . CoRR, (2022)Extreme Meta-Classification for Large-Scale Zero-Shot Retrieval., , , , , , , , and . KDD, page 3657-3666. ACM, (2024)This Explains That: Congruent Image-Report Generation for Explainable Medical Image Analysis with Cyclic Generative Adversarial Networks., , , , and . iMIMIC/TDA4MedicalData@MICCAI, volume 12929 of Lecture Notes in Computer Science, page 34-43. Springer, (2021)NGAME: Negative Mining-aware Mini-batching for Extreme Classification., , , , , , , , , and 9 other author(s). CoRR, (2022)CROSS-JEM: Accurate and Efficient Cross-encoders for Short-text Ranking Tasks., , , , , , , , and . CoRR, (2024)OAK: Enriching Document Representations using Auxiliary Knowledge for Extreme Classification., , , , , , , and . ICML, OpenReview.net, (2024)