# One-Shot Unsupervised Cross Domain Translation

arXiv: https://arxiv.org/abs/1806.06029 · HTML: https://mlsanity.com/paper/1806.06029/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2018.

## Built on by (most cited first, 30 of 35)

- [A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2205.06743/) (2022)
- [Image-to-Image Translation: Methods and Applications](https://mlsanity.com/paper/2101.08629/) (2021)
- [LFPT5: A Unified Framework for Lifelong Few-shot Language Learning Based on Prompt Tuning of T5](https://mlsanity.com/paper/2110.07298/) (2021)
- [MT3: Meta Test-Time Training for Self-Supervised Test-Time Adaption](https://mlsanity.com/paper/2103.16201/) (2021)
- [Source-Free Domain Adaptation for Image Segmentation](https://mlsanity.com/paper/2108.03152/) (2021)
- [One-shot Unsupervised Domain Adaptation with Personalized Diffusion Models](https://mlsanity.com/paper/2303.18080/) (2023)
- [Continual Training of Language Models for Few-Shot Learning](https://mlsanity.com/paper/2210.05549/) (2022)
- [Continual Few-shot Relation Learning via Embedding Space Regularization and Data Augmentation](https://mlsanity.com/paper/2203.02135/) (2022)
- [Style Mixing and Patchwise Prototypical Matching for One-Shot Unsupervised Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2112.04665/) (2021)
- [Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey](https://mlsanity.com/paper/2303.08557/) (2023)
- [Learning Instance-Specific Adaptation for Cross-Domain Segmentation](https://mlsanity.com/paper/2203.16530/) (2022)
- [SMAE: Few-shot Learning for HDR Deghosting with Saturation-Aware Masked Autoencoders](https://mlsanity.com/paper/2304.06914/) (2023)
- [What Can Knowledge Bring to Machine Learning? -- A Survey of Low-shot Learning for Structured Data](https://mlsanity.com/paper/2106.06410/) (2021)
- [Subgraph-aware Few-Shot Inductive Link Prediction via Meta-Learning](https://mlsanity.com/paper/2108.00954/) (2021)
- [AGI for Agriculture](https://mlsanity.com/paper/2304.06136/) (2023)
- [TTAPS: Test-Time Adaption by Aligning Prototypes using Self-Supervision](https://mlsanity.com/paper/2205.08731/) (2022)
- [Dual-path Frequency Discriminators for Few-shot Anomaly Detection](https://mlsanity.com/paper/2403.04151/) (2024)
- [MetaCloth: Learning Unseen Tasks of Dense Fashion Landmark Detection from a Few Samples](https://mlsanity.com/paper/2112.02763/) (2021)
- [Few-shot Image Generation Using Discrete Content Representation](https://mlsanity.com/paper/2207.10833/) (2022)
- [Super-Resolving Cross-Domain Face Miniatures by Peeking at One-Shot Exemplar](https://mlsanity.com/paper/2103.08863/) (2021)
- [Few-shot Semantic Image Synthesis Using StyleGAN Prior](https://mlsanity.com/paper/2103.14877/) (2021)
- [Metric Based Few-Shot Graph Classification](https://mlsanity.com/paper/2206.03695/) (2022)
- [Self-Supervision & Meta-Learning for One-Shot Unsupervised Cross-Domain Detection](https://mlsanity.com/paper/2106.03496/) (2021)
- [Guided Deep Metric Learning](https://mlsanity.com/paper/2206.02029/) (2022)
- [One-Shot Domain Adaptive and Generalizable Semantic Segmentation with Class-Aware Cross-Domain Transformers](https://mlsanity.com/paper/2212.07292/) (2022)
- [Domain-Scalable Unpaired Image Translation via Latent Space Anchoring](https://mlsanity.com/paper/2306.14879/) (2023)
- [Learning Gaze-aware Compositional GAN](https://mlsanity.com/paper/2405.20643/) (2024)
- [ACE: Zero-Shot Image to Image Translation via Pretrained Auto-Contrastive-Encoder](https://mlsanity.com/paper/2302.11705/) (2023)
- [MODIFY: Model-driven Face Stylization without Style Images](https://mlsanity.com/paper/2303.09831/) (2023)
- [Target-driven One-Shot Unsupervised Domain Adaptation](https://mlsanity.com/paper/2305.04628/) (2023)

## Abstract (arXiv)

Given a single image x from domain A and a set of images from domain B, our task is to generate the analogous of x in B. We argue that this task could be a key AI capability that underlines the ability of cognitive agents to act in the world and present empirical evidence that the existing unsupervised domain translation methods fail on this task. Our method follows a two step process. First, a variational autoencoder for domain B is trained. Then, given the new sample x, we create a variational autoencoder for domain A by adapting the layers that are close to the image in order to directly fit x, and only indirectly adapt the other layers. Our experiments indicate that the new method does as well, when trained on one sample x, as the existing domain transfer methods, when these enjoy a multitude of training samples from domain A. Our code is made publicly available at https://github.com/sagiebenaim/OneShotTranslation