{"id":15653030,"url":"https://github.com/edgarriba/ali-pytorch","last_synced_at":"2026-02-27T13:10:29.689Z","repository":{"id":80409896,"uuid":"86041491","full_name":"edgarriba/ali-pytorch","owner":"edgarriba","description":"Adversarially Learned Inference in 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unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":["deep-learning","generative-model"],"created_at":"2024-10-03T12:44:31.129Z","updated_at":"2026-02-27T13:10:29.659Z","avatar_url":"https://github.com/edgarriba.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Adversarially Learned Inference\nImplementation of paper [Aversarially Learned Inference](https://arxiv.org/abs/1606.00704) in Pytorch\n\n`main.py` includes training code for datasets\n- [X] SVHN\n- [ ] CIFAR10\n- [ ] CelebA\n\n`models.py` includes the network architectures for the different datasets as defined in the orginal paper\n\n## Usage\n```\nusage: main.py [-h] --dataset DATASET --dataroot DATAROOT [--workers WORKERS]\n               [--batch-size BATCH_SIZE] [--image-size IMAGE_SIZE] [--nc NC]\n               [--nz NZ] [--epochs EPOCHS] [--lr LR] [--beta1 BETA1]\n               [--beta2 BETA2] [--cuda] [--ngpu NGPU] [--gpu-id GPU_ID]\n               [--netGx NETGX] [--netGz NETGZ] [--netDz NETDZ] [--netDx NETDX]\n               [--netDxz NETDXZ] [--clamp_lower CLAMP_LOWER]\n               [--clamp_upper CLAMP_UPPER] [--experiment EXPERIMENT]\n\noptional arguments:\n  -h, --help            show this help message and exit\n  --dataset DATASET     cifar10 | svhn | celeba\n  --dataroot DATAROOT   path to dataset\n  --workers WORKERS     number of data loading workers\n  --batch-size BATCH_SIZE\n                        input batch size\n  --image-size IMAGE_SIZE\n                        the height / width of the input image to network\n  --nc NC               input image channels\n  --nz NZ               size of the latent z vector\n  --epochs EPOCHS       number of epochs to train for\n  --lr LR               learning rate for optimizer, default=0.00005\n  --beta1 BETA1         beta1 for adam. default=0.5\n  --beta2 BETA2         beta2 for adam. default=0.999\n  --cuda                enables cuda\n  --ngpu NGPU           number of GPUs to use\n  --gpu-id GPU_ID       id(s) for CUDA_VISIBLE_DEVICES\n  --netGx NETGX         path to netGx (to continue training)\n  --netGz NETGZ         path to netGz (to continue training)\n  --netDz NETDZ         path to netDz (to continue training)\n  --netDx NETDX         path to netDx (to continue training)\n  --netDxz NETDXZ       path to netDxz (to continue training)\n  --clamp_lower CLAMP_LOWER\n  --clamp_upper CLAMP_UPPER\n  --experiment EXPERIMENT\n                        Where to store samples and models\n```\n## Example\ncommand line example for training SVHN\n```\npython main.py --dataset svhn --dataroot . --experiment svhn_ali --cuda --ngpu 1 --gpu-id 1 --batch-size 100 --epochs 100 --image-size 32 --nz 256 --lr 1e-4 --beta1 0.5 --beta2 10e-3\n```\n\n## Cite\n```\n@article{DBLP:journals/corr/DumoulinBPLAMC16,\n  author    = {Vincent Dumoulin and\n               Ishmael Belghazi and\n               Ben Poole and\n               Alex Lamb and\n               Mart{\\'{\\i}}n Arjovsky and\n               Olivier Mastropietro and\n               Aaron C. Courville},\n  title     = {Adversarially Learned Inference},\n  journal   = {CoRR},\n  volume    = {abs/1606.00704},\n  year      = {2016},\n  url       = {http://arxiv.org/abs/1606.00704},\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fedgarriba%2Fali-pytorch","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fedgarriba%2Fali-pytorch","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fedgarriba%2Fali-pytorch/lists"}