{"id":15580425,"url":"https://github.com/salu133445/dan","last_synced_at":"2025-03-25T20:31:59.657Z","repository":{"id":111841042,"uuid":"159173550","full_name":"salu133445/dan","owner":"salu133445","description":"Source code for \"Towards a Deeper Understanding of Adversarial Losses under a Discriminative Adversarial Network Setting\"","archived":false,"fork":false,"pushed_at":"2022-09-01T18:35:57.000Z","size":8350,"stargazers_count":42,"open_issues_count":0,"forks_count":6,"subscribers_count":4,"default_branch":"master","last_synced_at":"2025-03-19T03:33:46.933Z","etag":null,"topics":["generative-adversarial-network","machine-learning"],"latest_commit_sha":null,"homepage":"https://salu133445.github.io/dan/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/salu133445.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.txt","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2018-11-26T13:25:12.000Z","updated_at":"2024-05-12T13:38:48.000Z","dependencies_parsed_at":null,"dependency_job_id":"eca46ffa-8e3e-4037-9535-6592e45f2b3c","html_url":"https://github.com/salu133445/dan","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/salu133445%2Fdan","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/salu133445%2Fdan/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/salu133445%2Fdan/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/salu133445%2Fdan/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/salu133445","download_url":"https://codeload.github.com/salu133445/dan/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245540345,"owners_count":20632144,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["generative-adversarial-network","machine-learning"],"created_at":"2024-10-02T19:25:35.534Z","updated_at":"2025-03-25T20:31:59.598Z","avatar_url":"https://github.com/salu133445.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# DANTest\n\nSource code for \"Towards a Deeper Understanding of Adversarial Losses under a Discriminative Adversarial Network Setting\"\n\n## Prerequisites\n\n\u003e __Below we assume the working directory is the repository root.__\n\n### Install dependencies\n\n- Using pipenv (recommended)\n\n  \u003e Make sure `pipenv` is installed. (If not, simply run `pip install --user pipenv`.)\n\n  ```sh\n  # Install the dependencies\n  pipenv install\n  # Activate the virtual environment\n  pipenv shell\n  ```\n\n- Using pip\n\n  ```sh\n  # Install the dependencies\n  pip install -r requirements.txt\n  ```\n\n### Prepare training data\n\n```sh\n# Download the training data\n./scripts/download_data.sh\n# Store the training data to shared memory\n./scripts/process_data.sh\n```\n\nYou can also download the MNIST handwritten digit database manually\n[here](http://yann.lecun.com/exdb/mnist/).\n\n## Scripts\n\nWe provide several shell scripts for easy managing the experiments. (See\n`scripts/README.md` for a detailed documentation.)\n\n\u003e __Below we assume the working directory is the repository root.__\n\n### Train a new model\n\n1. Run the following command to set up a new experiment with default settings.\n\n   ```sh\n   # Set up a new experiment (for one run only)\n   ./scripts/setup_exp.sh -r 1 \"./exp/my_experiment/\"\n   ```\n\n2. Modify the configuration files for different experimental settings. The\n   configuration file can be found at `./exp/my_experiment/config.yaml`.\n\n3. Train the model by running the following command.\n\n     ```sh\n     # Train the model (on GPU 0)\n     ./scripts/run_train.sh -c -g 0 \"./exp/my_experiment/\"\n     ```\n\n## Outputs\n\nFor each run, there will be three folders created in the experiment folder.\n\n- `logs/`: contain all the logs created\n- `model/`: contain the trained model\n- `src/`: contain a backup of the source code\n\nNote that the _validation results_ can be found in the `logs/` folder.\n\n## Paper\n\n__Towards a Deeper Understanding of Adversarial Losses under a Discriminative Adversarial Network Setting__\u003cbr\u003e\nHao-Wen Dong and Yi-Hsuan Yang\u003cbr\u003e\n_arXiv preprint arXiv:1901.08753_, 2019.\u003cbr\u003e\n[[website](https://salu133445.github.io/dan/)]\n[[paper](https://salu133445.github.io/dan/pdf/dan-arxiv-paper.pdf)]\n[[arxiv](https://arxiv.org/abs/1901.08753)]\n[[code](https://github.com/salu133445/dan)]\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsalu133445%2Fdan","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsalu133445%2Fdan","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsalu133445%2Fdan/lists"}