42 datasets found

Four domain adaptation datasets: Medical, IT, Koran and Subtitles.

Two types of datasets: traditional bilingual and domain adaptation datasets.

The Waymo Domain Adaptation Dataset is a dataset for domain adaptation in 3D object detection, containing 20 sequences with 3933 frames.

ELDA: Using Edges to Have an Edge on Semantic Segmentation Based UDA

A medical cross-modality domain adaptation benchmark proposed in [6] to validate the performance of our proposed unsupervised cross-modality domain adaptation method for...

MoDA proposes a self-supervised domain adaptation method for visual and dynamics corruptions in embodied agents.

MRI scans appearance significantly depends on scanning protocols and, consequently, the data-collection institution. These variations between clinical sites result in dramatic...

The Office-10 dataset is a more classic benchmark dataset from [17].

The ImageCLEF-DA1 dataset is also a benchmark dataset for domain adaptation, which contains 12 categories shared by three public datasets, Caltech- 256 (C), ImageNet ILSVRC 2012...

The Office-31 dataset is a standard benchmark for domain adaptation from [16], comprising 4,652 images and 31 categories collected from three distinct domains: Amazon (A), Webcam...

Continual Test Time Adaptation (CTTA) is re-required to adapt efficiently to continuous unseen domains while retaining previously learned knowledge.

The paper proposes the Variational Bi-domain Triplet Autoencoder (VBTA) that learns a joint distribution of objects from different domains with the help of the learning triplets...

A novel domain adaptation framework for medical image segmentation

The UPNA Synthetic dataset consists of 12 videos for each of 10 subjects; 120 videos in total with 38,800 frames.

A dataset for the study of domain adaptation solutions, composed of synthetic and real videos from seven gesture classes, from both the ground and air perspectives.

The paper proposes a technique for domain adaptation in stacked autoencoders using systematic dropout.

The Office-Home dataset is a visual domain adaptation task, where the goal is to adapt a model trained in a source domain to a target domain with different distributions.

The VisDA dataset is a large-scale dataset for unsupervised domain adaptation, which consists of 152,397 synthetic images and 55,388 real-world images from the real world.

The Office-Home dataset is a dataset for unsupervised domain adaptation, which consists of 15,500 images from 65 classes in 4 distinct domains: Artistic images (Ar), Clip-Art...

The Office-31 dataset is a widely-used dataset for UDA, which consists of 4652 images of 31 categories from three domains: DSLR (D), Amazon (A), and Webcam (W).