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 implementationsDatasets
Latest papers
Learning Generalized Segmentation for Foggy-scenes by Bi-directional Wavelet Guidance
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.
Unsupervised Domain Adaptation for Brain Vessel Segmentation through Transwarp Contrastive Learning
Unsupervised domain adaptation (UDA) aims to align the labelled source distribution with the unlabelled target distribution to obtain domain-invariant predictive models.
Source-Guided Similarity Preservation for Online Person Re-Identification
Our framework is based on the extraction of a support set composed of source images that maximizes the similarity with the target data.
Source-Free Unsupervised Domain Adaptation with Hypothesis Consolidation of Prediction Rationale
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.
We're Not Using Videos Effectively: An Updated Domain Adaptive Video Segmentation Baseline
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.
Continuous Unsupervised Domain Adaptation Using Stabilized Representations and Experience Replay
Our solution is based on stabilizing the learned internal distribution to enhances the model generalization on new domains.
D3GU: Multi-Target Active Domain Adaptation via Enhancing Domain Alignment
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.
VLLaVO: Mitigating Visual Gap through LLMs
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.
DTBS: Dual-Teacher Bi-directional Self-training for Domain Adaptation in Nighttime Semantic Segmentation
Because the one-directional knowledge transfer from a single teacher is insufficient to adapt to a large domain shift.
Online Continual Domain Adaptation for Semantic Image Segmentation Using Internal Representations
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.