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Twin Auxiliary Classifiers GAN (TAC-GAN) Mingming Gong* Yanwu Xu* - PowerPoint PPT Presentation

Dec 02, 2023 •585 likes •803 views

Twin Auxiliary Classifiers GAN (TAC-GAN) Mingming Gong* Yanwu Xu* Chunyuan Li Kun Zhang Kayhan Batmanghelich <latexit

  1. Twin Auxiliary Classifiers GAN (TAC-GAN) Mingming Gong* Yanwu Xu* Chunyuan Li Kun Zhang Kayhan Batmanghelich

  2. <latexit sha1_base64="VLEo6VgUnu2TnOxoOkqsMPXvyTo=">AB6HicbVDLTgJBEOzF+IL9ehlIjHxRHbRI9ELx4hkUcCGzI79MLI7OxmZtYECV/gxYPGePWTvPk3DrAHBSvpFLVne6uIBFcG9f9dnJr6xubW/ntws7u3v5B8fCoqeNUMWywWMSqHVCNgktsG4EthOFNAoEtoLR7cxvPaLSPJb3ZpygH9GB5CFn1Fip/tQrltyOwdZJV5GSpCh1it+dfsxSyOUhgmqdcdzE+NPqDKcCZwWuqnGhLIRHWDHUkj1P5kfuiUnFmlT8JY2ZKGzNXfExMaT2OAtsZUTPUy95M/M/rpCa89idcJqlByRaLwlQE5PZ16TPFTIjxpZQpri9lbAhVZQZm03BhuAtv7xKmpWyd1Gu1C9L1ZsjycwCmcgwdXUIU7qEDGCA8wyu8OQ/Oi/PufCxac042cwx/4Hz+AOqPjQI=</latexit> Generative adversarial networks(GAN) Noise ( ) Real/Fake z Discriminator (D) Generator (G) Minimax game between G and D 2

  3. <latexit sha1_base64="VLEo6VgUnu2TnOxoOkqsMPXvyTo=">AB6HicbVDLTgJBEOzF+IL9ehlIjHxRHbRI9ELx4hkUcCGzI79MLI7OxmZtYECV/gxYPGePWTvPk3DrAHBSvpFLVne6uIBFcG9f9dnJr6xubW/ntws7u3v5B8fCoqeNUMWywWMSqHVCNgktsG4EthOFNAoEtoLR7cxvPaLSPJb3ZpygH9GB5CFn1Fip/tQrltyOwdZJV5GSpCh1it+dfsxSyOUhgmqdcdzE+NPqDKcCZwWuqnGhLIRHWDHUkj1P5kfuiUnFmlT8JY2ZKGzNXfExMaT2OAtsZUTPUy95M/M/rpCa89idcJqlByRaLwlQE5PZ16TPFTIjxpZQpri9lbAhVZQZm03BhuAtv7xKmpWyd1Gu1C9L1ZsjycwCmcgwdXUIU7qEDGCA8wyu8OQ/Oi/PufCxac042cwx/4Hz+AOqPjQI=</latexit> Conditional GAN(CGAN) Noise ( ) Real Cat vs/Fake Cat z Input Label ( Cat ) Generator (G) Discriminator (D) Minimax game between G and D 3

  4. <latexit sha1_base64="VLEo6VgUnu2TnOxoOkqsMPXvyTo=">AB6HicbVDLTgJBEOzF+IL9ehlIjHxRHbRI9ELx4hkUcCGzI79MLI7OxmZtYECV/gxYPGePWTvPk3DrAHBSvpFLVne6uIBFcG9f9dnJr6xubW/ntws7u3v5B8fCoqeNUMWywWMSqHVCNgktsG4EthOFNAoEtoLR7cxvPaLSPJb3ZpygH9GB5CFn1Fip/tQrltyOwdZJV5GSpCh1it+dfsxSyOUhgmqdcdzE+NPqDKcCZwWuqnGhLIRHWDHUkj1P5kfuiUnFmlT8JY2ZKGzNXfExMaT2OAtsZUTPUy95M/M/rpCa89idcJqlByRaLwlQE5PZ16TPFTIjxpZQpri9lbAhVZQZm03BhuAtv7xKmpWyd1Gu1C9L1ZsjycwCmcgwdXUIU7qEDGCA8wyu8OQ/Oi/PufCxac042cwx/4Hz+AOqPjQI=</latexit> Auxiliary Classifier Conditional GAN(AC-GAN) Noise ( ) Real vs/Fake z Input Label Discriminator (D) Generator (G) ( Cat ) Output Label ( Cat ) Minimax game between G and D Auxiliary classifier C Classifier (C) 4

  5. Issues with AC-GAN Tends toward mode collapse Low diversity Apple Fish Tree Motorcycle Mountain CIFAR100 Imagenet1000 5

  6. <latexit sha1_base64="q+aoW/evF86+PO2QbLSAyfHLtpE=">AB7HicbVBNS8NAEJ3Ur1q/qh69LBahXkpSBT0WvXisYGqhDWznbRLN5uwuxFK6W/w4kERr/4gb/4bt20O2vpg4PHeDPzwlRwbVz32ymsrW9sbhW3Szu7e/sH5cOjlk4yxdBniUhUO6QaBZfoG24EtlOFNA4FPoaj25n/+IRK80Q+mHGKQUwHkecUWMlv9rstc975Ypbc+cgq8TLSQVyNHvlr24/YVmM0jBte54bmqCVWGM4HTUjfTmFI2ogPsWCpjDqYzI+dkjOr9EmUKFvSkLn6e2JCY63HcWg7Y2qGetmbif95ncxE18GEyzQzKNliUZQJYhIy+5z0uUJmxNgSyhS3txI2pIoyY/Mp2RC85ZdXSate8y5q9fvLSuMmj6MIJ3AKVfDgChpwB03wgQGHZ3iFN0c6L86787FoLTj5zDH8gfP5A9Fnjg=</latexit> <latexit sha1_base64="6PMEbKT3woBpZQ6mGVIbOsCfOX0=">AB7HicbVBNTwIxEJ3FL8Qv1KOXRmKCF7KLJnokevEIiQsksCHd0oWGtrtpuyZkw2/w4kFjvPqDvPlvLAHBV8yct7M5mZFyacaeO6305hY3Nre6e4W9rbPzg8Kh+ftHWcKkJ9EvNYdUOsKWeS+oYZTruJoliEnHbCyf3c7zxRpVksH80oYHAI8kiRrCxkl9tDbqXg3LFrbkLoHXi5aQCOZqD8ld/GJNUGkIx1r3PDcxQYaVYTWamfapgMsEj2rNUYkF1kC2OnaELqwxRFCtb0qCF+nsiw0LrqQhtp8BmrFe9ufif10tNdBtkTCapoZIsF0UpRyZG8/RkClKDJ9agoli9lZExlhYmw+JRuCt/ryOmnXa95Vrd6rjTu8jiKcAbnUAUPbqABD9AEHwgweIZXeHOk8+K8Ox/L1oKTz5zCHzifP9Lujgk=</latexit> 1D Mixture of Gaussian True Distribution Estimated Distribution by AC-GAN ( P X ) ( Q X ) 6

  7. <latexit sha1_base64="s/exqeaXa47KUfqvjKRaIZRa+aU=">AB6HicdVDLSgMxFL3js9ZX1aWbYBFcDTNT+3BXdOyBfuAdpBMmljM5khyQhl6Be4caGIWz/JnX9j+hBU9EDgcM65N4TJwp7Tgf1srq2vrGZm4rv72zu7dfODhsqziVhLZIzGPZDbCinAna0kxz2k0kxVHAaScYX838zj2VisXiRk8S6kd4KFjICNZGajZuC0XHdmqVUq2CHNtzneqFZ0jZK5c8F7m2M0cRljD59/4gJmlEhSYcK9VznUT7GZaEU6n+X6qaILJGA9pz1CBI6r8bL7oFJ0aZYDCWJonNJqr3ycyHCk1iQKTjLAeqd/eTPzL6U6rPkZE0mqSCLj8KUIx2j2dVowCQlmk8MwUQysysiIywx0abvCnh61L0P2l7tluy3eZ5sX65rCMHx3ACZ+BCFepwDQ1oAQEKD/AEz9ad9Wi9WK+L6Iq1nDmCH7DePgEQVo0d</latexit> <latexit sha1_base64="x4BK2VgOpXHSkC8FBu7BJjsRKFs=">AB6HicdVDJSgNBEO2JW4xb1KOXxiB4GnqSIcst6MVjAmaBZAg9nUrSpmehu0cIQ7AiwdFvPpJ3vwbO4ugog8KHu9VUVXPjwVXmpAPK7OxubW9k93N7e0fHB7lj0/aKkokgxaLRCS7PlUgeAgtzbWAbiyBr6Aj+9Xvide5CKR+GtnsXgBXQc8hFnVBup2RzkC8QulhzXJZjYbpmQSs0QUquWykXs2GSJAlqjMci/94cRSwINRNUqZ5DYu2lVGrOBMxz/URBTNmUjqFnaEgDUF6PHSOL4wyxKNImgo1XqrfJ1IaKDULfNMZUD1Rv72F+JfXS/So6qU8jBMNIVstGiUC6wgvsZDLoFpMTOEMsnNrZhNqKRMm2xyJoSvT/H/pF20nZLtN1C/WodRxadoXN0iRxUQXV0gxqohRgC9ICe0LN1Zz1aL9brqjVjrWdO0Q9Yb58OCo0c</latexit> <latexit sha1_base64="EBy45Qk2sUIZo/T8IjVOh4eaNzg=">AB6XicbVBNS8NAEJ3Ur1q/qh69LBbBU0lU0GPRi8cq9kPaUDbTbt0swm7E6GE/gMvHhTx6j/y5r9x2+agrQ8GHu/NMDMvSKQw6LrfTmFldW19o7hZ2tre2d0r7x80TZxqxhslrFuB9RwKRvoEDJ24nmNAokbwWjm6nfeuLaiFg94DjhfkQHSoSCUbTSfuxV64VXcGsky8nFQgR71X/ur2Y5ZGXCGT1JiO5yboZ1SjYJPSt3U8ISyER3wjqWKRtz42ezSCTmxSp+EsbalkMzU3xMZjYwZR4HtjCgOzaI3Ff/zOimGV34mVJIiV2y+KEwlwZhM3yZ9oTlDObaEMi3srYQNqaYMbTglG4K3+PIyaZ5VvfOqd3dRqV3ncRThCI7hFDy4hBrcQh0awCEZ3iFN2fkvDjvzse8teDkM4fwB87nD2LkjUI=</latexit> AC-GAN: Density Matching Point of View data distribution P Q estimated distribution joint XY 7

  8. <latexit sha1_base64="8qOjKVUOV3WtxLlf9fJ/ZGLVadQ=">AB6HicbVBNS8NAEJ3Ur1q/qh69LBbBU0lU0GPRi8cW7Ae0oWy2k3btZhN2N0IJ/QVePCji1Z/kzX/jts1BWx8MPN6bYWZekAiujet+O4W19Y3NreJ2aWd3b/+gfHjU0nGqGDZLGLVCahGwSU2DTcCO4lCGgUC28H4bua3n1BpHsHM0nQj+hQ8pAzaqzU6PTLFbfqzkFWiZeTCuSo98tfvUHM0gilYJq3fXcxPgZVYzgdNSL9WYUDamQ+xaKmE2s/mh07JmVUGJIyVLWnIXP09kdFI60kU2M6ImpFe9mbif143NeGNn3GZpAYlWywKU0FMTGZfkwFXyIyYWEKZ4vZWwkZUWZsNiUbgrf8ipXVS9y6rXuKrUbvM4inACp3AOHlxDe6hDk1gPAMr/DmPDovzrvzsWgtOPnMfyB8/kDtrWM3w=</latexit> <latexit sha1_base64="s/exqeaXa47KUfqvjKRaIZRa+aU=">AB6HicdVDLSgMxFL3js9ZX1aWbYBFcDTNT+3BXdOyBfuAdpBMmljM5khyQhl6Be4caGIWz/JnX9j+hBU9EDgcM65N4TJwp7Tgf1srq2vrGZm4rv72zu7dfODhsqziVhLZIzGPZDbCinAna0kxz2k0kxVHAaScYX838zj2VisXiRk8S6kd4KFjICNZGajZuC0XHdmqVUq2CHNtzneqFZ0jZK5c8F7m2M0cRljD59/4gJmlEhSYcK9VznUT7GZaEU6n+X6qaILJGA9pz1CBI6r8bL7oFJ0aZYDCWJonNJqr3ycyHCk1iQKTjLAeqd/eTPzL6U6rPkZE0mqSCLj8KUIx2j2dVowCQlmk8MwUQysysiIywx0abvCnh61L0P2l7tluy3eZ5sX65rCMHx3ACZ+BCFepwDQ1oAQEKD/AEz9ad9Wi9WK+L6Iq1nDmCH7DePgEQVo0d</latexit> <latexit sha1_base64="x4BK2VgOpXHSkC8FBu7BJjsRKFs=">AB6HicdVDJSgNBEO2JW4xb1KOXxiB4GnqSIcst6MVjAmaBZAg9nUrSpmehu0cIQ7AiwdFvPpJ3vwbO4ugog8KHu9VUVXPjwVXmpAPK7OxubW9k93N7e0fHB7lj0/aKkokgxaLRCS7PlUgeAgtzbWAbiyBr6Aj+9Xvide5CKR+GtnsXgBXQc8hFnVBup2RzkC8QulhzXJZjYbpmQSs0QUquWykXs2GSJAlqjMci/94cRSwINRNUqZ5DYu2lVGrOBMxz/URBTNmUjqFnaEgDUF6PHSOL4wyxKNImgo1XqrfJ1IaKDULfNMZUD1Rv72F+JfXS/So6qU8jBMNIVstGiUC6wgvsZDLoFpMTOEMsnNrZhNqKRMm2xyJoSvT/H/pF20nZLtN1C/WodRxadoXN0iRxUQXV0gxqohRgC9ICe0LN1Zz1aL9brqjVjrWdO0Q9Yb58OCo0c</latexit> <latexit sha1_base64="hetRNmVqKWZen/S491Dadp4Pp4=">AB6HicbVBNS8NAEJ34WetX1aOXxSJ4KokKeix68diC/ZA2lM120q7dbMLuRihv8CLB0W8+pO8+W/ctjlo64OBx3szMwLEsG1cd1vZ2V1bX1js7BV3N7Z3dsvHRw2dZwqhg0Wi1i1A6pRcIkNw43AdqKQRoHAVjC6nfqtJ1Sax/LejBP0IzqQPOSMGivVH3qlsltxZyDLxMtJGXLUeqWvbj9maYTSMEG17nhuYvyMKsOZwEmxm2pMKBvRAXYslTRC7WezQyfk1Cp9EsbKljRkpv6eyGik9TgKbGdEzVAvelPxP6+TmvDaz7hMUoOSzReFqSAmJtOvSZ8rZEaMLaFMcXsrYUOqKDM2m6INwVt8eZk0zyveRcWrX5arN3kcBTiGEzgD6gCndQgwYwQHiGV3hzHp0X5935mLeuOPnMEfyB8/kDuDmM4A=</latexit> <latexit sha1_base64="EBy45Qk2sUIZo/T8IjVOh4eaNzg=">AB6XicbVBNS8NAEJ3Ur1q/qh69LBbBU0lU0GPRi8cq9kPaUDbTbt0swm7E6GE/gMvHhTx6j/y5r9x2+agrQ8GHu/NMDMvSKQw6LrfTmFldW19o7hZ2tre2d0r7x80TZxqxhslrFuB9RwKRvoEDJ24nmNAokbwWjm6nfeuLaiFg94DjhfkQHSoSCUbTSfuxV64VXcGsky8nFQgR71X/ur2Y5ZGXCGT1JiO5yboZ1SjYJPSt3U8ISyER3wjqWKRtz42ezSCTmxSp+EsbalkMzU3xMZjYwZR4HtjCgOzaI3Ff/zOimGV34mVJIiV2y+KEwlwZhM3yZ9oTlDObaEMi3srYQNqaYMbTglG4K3+PIyaZ5VvfOqd3dRqV3ncRThCI7hFDy4hBrcQh0awCEZ3iFN2fkvDjvzse8teDkM4fwB87nD2LkjUI=</latexit> <latexit sha1_base64="dAdtkaf8MKyPz11odEQxhtSVUts=">AAAB6nicbVBNS8NAEJ34WetX1aOXxSJ4KokKeix68VjRfkgbyma7aZduNmF3IpTYn+DFgyJe/UXe/Ddu2xy09cHA470ZZuYFiRQGXffbWVpeWV1bL2wUN7e2d3ZLe/sNE6ea8TqLZaxbATVcCsXrKFDyVqI5jQLJm8HweuI3H7k2Ilb3OEq4H9G+EqFgFK109/DU6pbKbsWdgiwSLydlyFHrlr46vZilEVfIJDWm7bkJ+hnVKJjk42InNTyhbEj7vG2pohE3fjY9dUyOrdIjYaxtKSRT9fdERiNjRlFgOyOKAzPvTcT/vHaK4aWfCZWkyBWbLQpTSTAmk79JT2jOUI4soUwLeythA6opQ5tO0Ybgzb+8SBqnFe+s4t2el6tXeRwFOIQjOAEPLqAKN1CDOjDowzO8wpsjnRfn3fmYtS45+cwB/IHz+QNEn43I</latexit> AC-GAN: Density Matching Point of View data P Q estimated joint XY data X label Y Y | X conditional 8

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Joint Distributions, Independence Covariance and Correlation 18.05 Spring 2014 Jeremy Orloff and Jonathan Bloom X \ Y 1 2 3 4 5 6 1 1/36 1/36 1/36 1/36 1/36 1/36 2 1/36 1/36 1/36 1/36 1/36 1/36 3 1/36 1/36 1/36 1/36 1/36

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