Papers › Unsupervised Cross-Domain Image Generation

Unsupervised Cross-Domain Image Generation

7 Nov 2016arXiv:1611.02200archive 2025-07-28

Yaniv Taigman, Adam Polyak, Lior Wolf

We study the problem of transferring a sample in one domain to an analog sample in another domain. Given two related domains, S and T, we would like to learn a generative function G that maps an input sample from S to the domain T, such that the output of a given function f, which accepts inputs in either domains, would remain unchanged. Other than the function f, the training data is unsupervised and consist of a set of samples from each domain. The Domain Transfer Network (DTN) we present employs a compound loss function that includes a multiclass GAN loss, an f-constancy component, and a regularizing component that encourages G to map samples from T to themselves. We apply our method to visual domains including digits and face images and demonstrate its ability to generate convincing novel images of previously unseen entities, while preserving their identity.

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deepakprabakar96/DTN mentioned on GitHubtfMIT report
kaonashi-tyc/zi2zi mentioned on GitHubtfApache-2.0 report
taesungp/contrastive-unpaired-translation mentioned on GitHubpytorchNOASSERTION report
yunjey/domain-transfer-network mentioned on GitHubtfMIT report

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1ran · honoured contract
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conv2d_bn deepakprabakar96/DTN/face/facenet/model.py community (archive-listed) ran · our draft was wrong MIT (permissive) · 6c4df2f8f7ef8073 · report
scaling deepakprabakar96/DTN/face/facenet/model.py community (archive-listed) ran · honoured contract fingerprinted MIT (permissive) · 3587611134f2fad7 · report
InceptionResNetV1 deepakprabakar96/DTN/face/facenet/model.py community (archive-listed) unverified MIT (permissive) · 3b162b9c2907af3c · report
align_images deepakprabakar96/DTN/face/facenet/preprocessing.py community (archive-listed) unverified MIT (permissive) · 7b7f54714c01afcb · report
batch_norm kaonashi-tyc/zi2zi/model/ops.py community (archive-listed) unverified Apache-2.0 (permissive) · b9f34d6c4b16e50c · report
conv2d kaonashi-tyc/zi2zi/model/ops.py community (archive-listed) unverified Apache-2.0 (permissive) · fcfc3f2f2160edcd · report
deconv2d kaonashi-tyc/zi2zi/model/ops.py community (archive-listed) unverified Apache-2.0 (permissive) · 53db3506d02a13f1 · report
draw_example kaonashi-tyc/zi2zi/font2img.py community (archive-listed) unverified Apache-2.0 (permissive) · 601e95841dbdec47 · report
draw_single_char kaonashi-tyc/zi2zi/font2img.py community (archive-listed) unverified Apache-2.0 (permissive) · 1f95dd130aa17598 · report
filter_recurring_hash kaonashi-tyc/zi2zi/font2img.py community (archive-listed) unverified Apache-2.0 (permissive) · 8ce2794b7267398c · report
get_batch_iter kaonashi-tyc/zi2zi/model/dataset.py community (archive-listed) unverified Apache-2.0 (permissive) · 1063b585a5249011 · report
l2_normalize deepakprabakar96/DTN/face/facenet/preprocessing.py community (archive-listed) unverified MIT (permissive) · 9e7babae415f5497 · report
load_svhn_data deepakprabakar96/DTN/digits/train_svhn.py community (archive-listed) unverified MIT (permissive) · 7d1b2d3f33639fc4 · report
normalize_image kaonashi-tyc/zi2zi/model/utils.py community (archive-listed) unverified Apache-2.0 (permissive) · 45420560ebfbf987 · report
pad_seq kaonashi-tyc/zi2zi/model/utils.py community (archive-listed) unverified Apache-2.0 (permissive) · 85e94d4d778cf489 · report
prewhiten deepakprabakar96/DTN/face/facenet/preprocessing.py community (archive-listed) unverified MIT (permissive) · 7f7d0b4bf014ee48 · report
resize_images yunjey/domain-transfer-network/prepro.py community (archive-listed) unverified MIT (permissive) · 4c870b875bc1404a · report
train_mnist deepakprabakar96/DTN/digits/mnist_classifier.py community (archive-listed) unverified MIT (permissive) · d057decb606addc4 · report

Tasks

Domain AdaptationImage GenerationImage-to-Image TranslationUnsupervised Image-To-Image Translation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Unsupervised Image-To-Image Translation SVNH-to-MNIST DTN Classification Accuracy 84.4% #2 of 4 Archive leaderboard report

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Methods

Convolution

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