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Version\" src=\"https://img.shields.io/pypi/v/calotron\"\u003e\u003c/a\u003e\n  \u003ca href=\"https://github.com/mbarbetti/calotron/blob/main/LICENSE\"\u003e\u003cimg alt=\"GitHub - License\" src=\"https://img.shields.io/github/license/mbarbetti/calotron\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"https://github.com/mbarbetti/calotron/actions/workflows/tests.yml\"\u003e\u003cimg alt=\"GitHub - Tests\" src=\"https://github.com/mbarbetti/calotron/actions/workflows/tests.yml/badge.svg?branch=main\"\u003e\u003c/a\u003e\n  \u003ca href=\"https://codecov.io/gh/mbarbetti/calotron\"\u003e\u003cimg alt=\"Codecov\" src=\"https://codecov.io/gh/mbarbetti/calotron/branch/main/graph/badge.svg?token=DRG8BWC9RR\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"https://github.com/mbarbetti/calotron/actions/workflows/style.yml\"\u003e\u003cimg alt=\"GitHub - Style\" src=\"https://github.com/mbarbetti/calotron/actions/workflows/style.yml/badge.svg?branch=main\"\u003e\u003c/a\u003e\n  \u003ca href=\"https://github.com/astral-sh/ruff\"\u003e\u003cimg alt=\"Ruff\" src=\"https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json\" style=\"max-width:100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\n\u003c!--\n[![Docker - Version](https://img.shields.io/docker/v/mbarbetti/calotron?label=docker)](https://hub.docker.com/r/mbarbetti/calotron)\n--\u003e\n\n### Transformers\n\n|         Models        | Implementation | Generative ability* | Test | Design inspired by |\n|:---------------------:|:--------------:|:-------------------:|:----:|:------------------:|\n|     `Transformer`     |       ✅       |          ❌          |  ✅  | [1](https://arxiv.org/abs/1706.03762), [4](https://arxiv.org/abs/2004.08249) |\n| `OptionalTransformer` |       ✅       |          ❌          |  ✅  | [1](https://arxiv.org/abs/1706.03762), [4](https://arxiv.org/abs/2004.08249) |\n|  `MaskedTransformer`  |       🛠️       |          ❌          |  ❌  | |\n|    `GigaGenerator`    |       ✅       |          ✅          |  ✅  | [5](https://arxiv.org/abs/2303.05511), [6](https://arxiv.org/abs/2107.04589) |\n\n*TBA\n\n### Discriminators\n\n|          Models         |  Algorithm  | Implementation | Test | Design inspired by |\n|:-----------------------:|:-----------:|:--------------:|:----:|:------------------:|\n|     `Discriminator`     |   DeepSets  |       ✅       |  ✅  | [2](https://cds.cern.ch/record/2721094), [3](https://arxiv.org/abs/1703.06114) |\n| `PairwiseDiscriminator` |   DeepSets  |       ✅       |  ✅  | [2](https://cds.cern.ch/record/2721094), [3](https://arxiv.org/abs/1703.06114) |\n|    `GNNDiscriminator`   |     GNN     |       🛠️       |  ❌  | |\n|   `GigaDiscriminator`   | Transformer |       ✅       |  ✅  | [5](https://arxiv.org/abs/2303.05511), [6](https://arxiv.org/abs/2107.04589), [7](https://arxiv.org/abs/2006.04710) |\n\n### References\n1. A. Vaswani _et al._, \"Attention Is All You Need\", [arXiv:1706.03762](https://arxiv.org/abs/1706.03762)\n2. N.M. Hartman, M. Kagan and R. Teixeira De Lima, \"Deep Sets for Flavor Tagging on the ATLAS Experiment\", [ATL-PHYS-PROC-2020-043](https://cds.cern.ch/record/2721094)\n3. M. Zaheer _et al._, \"Deep Sets\", [arXiv:1703.06114](https://arxiv.org/abs/1703.06114)\n4. L. Liu _et al._, \"Understanding the Difficulty of Training Transformers\", [arXiv:2004.08249](https://arxiv.org/abs/2004.08249)\n5. M. Kang _et al._, \"Scaling up GANs for Text-to-Image Synthesis\", [arXiv:2303.05511](https://arxiv.org/abs/2303.05511)\n6. K. Lee _et al._, \"ViTGAN: Training GANs with Vision Transformers\", [arXiv:2107.04589](https://arxiv.org/abs/2107.04589)\n7. H. Kim, G. Papamakarios and A. Mnih, \"The Lipschitz Constant of Self-Attention\", [arXiv:2006.04710](https://arxiv.org/abs/2006.04710)\n\n### Credits\nTransformer implementation freely inspired by the TensorFlow tutorial [Neural machine translation with a Transformer and Keras](https://www.tensorflow.org/text/tutorials/transformer) and the Keras tutorial [Image classification with Vision Transformer](https://keras.io/examples/vision/image_classification_with_vision_transformer).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmbarbetti%2Fcalotron","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmbarbetti%2Fcalotron","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmbarbetti%2Fcalotron/lists"}