81 datasets found

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...

The dataset used in this paper for unsupervised domain adaptation.

Language models have been commonly adopted to boost the performance of automatic speech recognition (ASR) particularly in domain adaptation tasks. Conventional way of LM...

Select-by-Distinctive-Margin (SDM) is a simple yet effective active learning method for active domain adaptation.

The Office-31 dataset is a benchmark dataset for domain adaptation, containing images from the Amazon domain.

The Office+Caltech-10 dataset is a benchmark dataset for domain adaptation, combining Office-10 and Caltech-10 datasets.

The proposed method is implemented by the Pytorch framework using a NVIDIA GeForce RTX 3090 GPU with 24GB memory.

The PointDA-10 dataset consists of three subsets of three widely-used datasets: ShapeNet, ModelNet, and ScanNet. All three subsets share the same ten distinct classes.

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 Hyper-Kvasir→Piccolo task is a domain adaptation task for semantic segmentation, where the source domain is Hyper-Kvasir and the target domain is Piccolo.

The Synthia→Cityscapes task is a domain adaptation task for semantic segmentation, where the source domain is Synthia and the target domain is Cityscapes.

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).

The dataset used for out-of-distribution segmentation, zero-shot semantic segmentation, and domain adaptation.

PODIA-3D: Domain Adaptation of 3D Generative Model Across Large Domain Gap Using Pose-Preserved Text-to-Image Diffusion

Biwi Kinect is a real-world dataset containing over 15K images of 20 people.