Unsupervised Domain Adaptation

710 papers with code • 36 benchmarks • 31 datasets

Unsupervised Domain Adaptation is a learning framework to transfer knowledge learned from source domains with a large number of annotated training examples to target domains with unlabeled data only.

Source: Domain-Specific Batch Normalization for Unsupervised Domain Adaptation

Libraries

Use these libraries to find Unsupervised Domain Adaptation models and implementations

Learning Generalized Segmentation for Foggy-scenes by Bi-directional Wavelet Guidance

BiQiWHU/BWG Association for the Advancement of Artificial Intelligence (AAAI) 2024

We argue that an ideal segmentation model that can be well generalized to foggy-scenes need to simultaneously enhance the content, de-correlate the urban-scene style and de-correlate the fog style.

3
28 Feb 2024

Unsupervised Domain Adaptation for Brain Vessel Segmentation through Transwarp Contrastive Learning

fmlinks/domain 23 Feb 2024

Unsupervised domain adaptation (UDA) aims to align the labelled source distribution with the unlabelled target distribution to obtain domain-invariant predictive models.

6
23 Feb 2024

Source-Guided Similarity Preservation for Online Person Re-Identification

ramimmhamza/s2p 23 Feb 2024

Our framework is based on the extraction of a support set composed of source images that maximizes the similarity with the target data.

4
23 Feb 2024

Source-Free Unsupervised Domain Adaptation with Hypothesis Consolidation of Prediction Rationale

ganperf/hcpr 2 Feb 2024

Source-Free Unsupervised Domain Adaptation (SFUDA) is a challenging task where a model needs to be adapted to a new domain without access to target domain labels or source domain data.

4
02 Feb 2024

We're Not Using Videos Effectively: An Updated Domain Adaptive Video Segmentation Baseline

simarkareer/unifiedvideoda 1 Feb 2024

While the vast majority of prior work has studied this as a frame-level Image-DAS problem, a few Video-DAS works have sought to additionally leverage the temporal signal present in adjacent frames.

16
01 Feb 2024

Continuous Unsupervised Domain Adaptation Using Stabilized Representations and Experience Replay

rostami-m/ldacid 31 Jan 2024

Our solution is based on stabilizing the learned internal distribution to enhances the model generalization on new domains.

1
31 Jan 2024

D3GU: Multi-Target Active Domain Adaptation via Enhancing Domain Alignment

lzhangbj/d3gu 10 Jan 2024

Unsupervised domain adaptation (UDA) for image classification has made remarkable progress in transferring classification knowledge from a labeled source domain to an unlabeled target domain, thanks to effective domain alignment techniques.

1
10 Jan 2024

VLLaVO: Mitigating Visual Gap through LLMs

LL-a-VO/VLLaVO 6 Jan 2024

Recent advances achieved by deep learning models rely on the independent and identically distributed assumption, hindering their applications in real-world scenarios with domain shifts.

13
06 Jan 2024

DTBS: Dual-Teacher Bi-directional Self-training for Domain Adaptation in Nighttime Semantic Segmentation

hf618/dtbs 2 Jan 2024

Because the one-directional knowledge transfer from a single teacher is insufficient to adapt to a large domain shift.

13
02 Jan 2024

Online Continual Domain Adaptation for Semantic Image Segmentation Using Internal Representations

serbanstan/mas3-continual 2 Jan 2024

Semantic segmentation models trained on annotated data fail to generalize well when the input data distribution changes over extended time period, leading to requiring re-training to maintain performance.

2
02 Jan 2024