One-shot Unsupervised Domain Adaptation
5 papers with code • 2 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in One-shot Unsupervised Domain Adaptation
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SYNTHIA
GTA5
Most implemented papers
Adversarial Style Mining for One-Shot Unsupervised Domain Adaptation
RoyalVane/ASM
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NeurIPS 2020
We aim at the problem named One-Shot Unsupervised Domain Adaptation.
Style Mixing and Patchwise Prototypical Matching for One-Shot Unsupervised Domain Adaptive Semantic Segmentation
w-zx-y/sm-ppm
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In this paper, we tackle the problem of one-shot unsupervised domain adaptation (OSUDA) for semantic segmentation where the segmentors only see one unlabeled target image during training.
PODA: Prompt-driven Zero-shot Domain Adaptation
astra-vision/poda
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ICCV 2023
In this paper, we propose the task of 'Prompt-driven Zero-shot Domain Adaptation', where we adapt a model trained on a source domain using only a general description in natural language of the target domain, i. e., a prompt.
One-shot Unsupervised Domain Adaptation with Personalized Diffusion Models
yasserben/datum
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Departing from the common notion of transferring only the target ``texture'' information, we leverage text-to-image diffusion models (e. g., Stable Diffusion) to generate a synthetic target dataset with photo-realistic images that not only faithfully depict the style of the target domain, but are also characterized by novel scenes in diverse contexts.
Learnable Data Augmentation for One-Shot Unsupervised Domain Adaptation
This paper presents a classification framework based on learnable data augmentation to tackle the One-Shot Unsupervised Domain Adaptation (OS-UDA) problem.
