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25 dataset results for Domain Generalization

Fashion-MNIST is a dataset comprising of 28×28 grayscale images of 70,000 fashion products from 10 categories, with 7,000 images per category. The training set has 60,000 images and the test set has 10,000 images. Fashion-MNIST shares the same image size, data format and the structure of training and testing splits with the original MNIST.

2,781 PAPERS • 17 BENCHMARKS

Office-Home

Office-Home is a benchmark dataset for domain adaptation which contains 4 domains where each domain consists of 65 categories. The four domains are: Art – artistic images in the form of sketches, paintings, ornamentation, etc.; Clipart – collection of clipart images; Product – images of objects without a background and Real-World – images of objects captured with a regular camera. It contains 15,500 images, with an average of around 70 images per class and a maximum of 99 images in a class.

935 PAPERS • 11 BENCHMARKS

DomainNet

DomainNet is a dataset of common objects in six different domain. All domains include 345 categories (classes) of objects such as Bracelet, plane, bird and cello. The domains include clipart: collection of clipart images; real: photos and real world images; sketch: sketches of specific objects; infograph: infographic images with specific object; painting artistic depictions of objects in the form of paintings and quickdraw: drawings of the worldwide players of game “Quick Draw!”.

604 PAPERS • 10 BENCHMARKS

PACS (Photo-Art-Cartoon-Sketch)

PACS is an image dataset for domain generalization. It consists of four domains, namely Photo (1,670 images), Art Painting (2,048 images), Cartoon (2,344 images) and Sketch (3,929 images). Each domain contains seven categories.

564 PAPERS • 7 BENCHMARKS

ImageNet-C

ImageNet-C is an open source data set that consists of algorithmically generated corruptions (blur, noise) applied to the ImageNet test-set.

510 PAPERS • 3 BENCHMARKS

ImageNet-R (ImageNet-Rendition)

ImageNet-R(endition) contains art, cartoons, deviantart, graffiti, embroidery, graphics, origami, paintings, patterns, plastic objects, plush objects, sculptures, sketches, tattoos, toys, and video game renditions of ImageNet classes.

341 PAPERS • 6 BENCHMARKS

ImageNet-A

The ImageNet-A dataset consists of real-world, unmodified, and naturally occurring examples that are misclassified by ResNet models.

318 PAPERS • 5 BENCHMARKS

ImageNet-Sketch

ImageNet-Sketch data set consists of 50,889 images, approximately 50 images for each of the 1000 ImageNet classes. The data set is constructed with Google Image queries "sketch of ", where is the standard class name. Only within the "black and white" color scheme is searched. 100 images are initially queried for every class, and the pulled images are cleaned by deleting the irrelevant images and images that are for similar but different classes. For some classes, there are less than 50 images after manually cleaning, and then the data set is augmented by flipping and rotating the images.

205 PAPERS • 3 BENCHMARKS

Stylized ImageNet

The Stylized-ImageNet dataset is created by removing local texture cues in ImageNet while retaining global shape information on natural images via AdaIN style transfer. This nudges CNNs towards learning more about shapes and less about local textures.

101 PAPERS • 1 BENCHMARK

WildDash

WildDash is a benchmark evaluation method is presented that uses the meta-information to calculate the robustness of a given algorithm with respect to the individual hazards.

46 PAPERS • 2 BENCHMARKS

Watercolor2k

Watercolor2k is a dataset used for cross-domain object detection which contains 2k watercolor images with image and instance-level annotations.

34 PAPERS • 4 BENCHMARKS

ImageNet-P

ImageNet-P consists of noise, blur, weather, and digital distortions. The dataset has validation perturbations; has difficulty levels; has CIFAR-10, Tiny ImageNet, ImageNet 64 × 64, standard, and Inception-sized editions; and has been designed for benchmarking not training networks. ImageNet-P departs from ImageNet-C by having perturbation sequences generated from each ImageNet validation image. Each sequence contains more than 30 frames, so to counteract an increase in dataset size and evaluation time only 10 common perturbations are used.

28 PAPERS • 1 BENCHMARK

NICO++

The goal of NICO Challenge is to facilitate the OOD (Out-of-Distribution) generalization in visual recognition through promoting the research on the intrinsic learning mechanisms with native invariance and generalization ability. The training data is a mixture of several observed contexts while the test data is composed of unseen contexts. Participants are tasked with developing reliable algorithms across different contexts (domains) to improve the generalization ability of models.

24 PAPERS • NO BENCHMARKS YET

VLCS

VLCS is a dataset to test for domain generalization.

24 PAPERS • NO BENCHMARKS YET

VizWiz-Classification

Our goal is to improve upon the status quo for designing image classification models trained in one domain that perform well on images from another domain. Complementing existing work in robustness testing, we introduce the first test dataset for this purpose which comes from an authentic use case where photographers wanted to learn about the content in their images. We built a new test set using 8,900 images taken by people who are blind for which we collected metadata to indicate the presence versus absence of 200 ImageNet object categories. We call this dataset VizWiz-Classification.

21 PAPERS • 3 BENCHMARKS

MSK

The MSK dataset is a dataset for lesion recognition from the Memorial Sloan-Kettering Cancer Center. It is used as part of the ISIC lesion recognition challenges.

12 PAPERS • NO BENCHMARKS YET

NICO

NICO (Non-I.I.D. Image dataset with Contexts)

I.I.D. hypothesis between training and testing data is the basis of numerous image classification methods. Such property can hardly be guaranteed in practice where the Non-IIDness is common, causing in- stable performances of these models. In literature, however, the Non-I.I.D. image classification problem is largely understudied. A key reason is lacking of a well-designed dataset to support related research. In this paper, we construct and release a Non-I.I.D. image dataset called NICO, which uses contexts to create Non-IIDness consciously. Compared to other datasets, extended analyses prove NICO can support various Non-I.I.D. situations with sufficient flexibility. Meanwhile, we propose a baseline model with Con- vNet structure for General Non-I.I.D. image classification, where distribution of testing data is unknown but different from training data. The experimental results demonstrate that NICO can well support the training of ConvNet model from scratch, and a batch balancing modul

8 PAPERS • 2 BENCHMARKS

Sims4Action

The Sims4Action Dataset: a videogame-based dataset for Synthetic→Real domain adaptation for human activity recognition.

5 PAPERS • NO BENCHMARKS YET

Wild-Time

Wild-Time is a benchmark of 5 datasets that reflect temporal distribution shifts arising in a variety of real-world applications, including patient prognosis and news classification. On these datasets, we systematically benchmark 13 prior approaches, including methods in domain generalization, continual learning, self-supervised learning, and ensemble learning.

4 PAPERS • NO BENCHMARKS YET

All-day CityScapes

We design an all-day semantic segmentation benchmark all-day CityScapes. It is the first semantic segmentation benchmark that contains samples from all-day scenarios, i.e., from dawn to night. Our dataset will be made publicly available at [https://isis-data.science.uva.nl/cv/1ADcityscape.zip].

3 PAPERS • 1 BENCHMARK

Prostate MRI Segmentation Dataset

This prostate MRI segmentation dataset is collected from six different data sources.

3 PAPERS • NO BENCHMARKS YET

CFC (Caltech Fish Counting Dataset)

Caltech Fish Counting Dataset (CFC) is a large-scale dataset for detecting, tracking, and counting fish in sonar videos. This dataset contains over 1,500 videos sourced from seven different sonar cameras.

2 PAPERS • NO BENCHMARKS YET

Super-CLEVR

Super-CLEVR is a dataset for Visual Question Answering (VQA) where different factors in VQA domain shifts can be isolated in order that their effects can be studied independently. It contains 21 vehicle models belonging to 5 categories, with controllable attributes. Four factors are considered: visual complexity, question redundancy, concept distribution and concept compositionality.

2 PAPERS • NO BENCHMARKS YET

WHYSHIFT

In our benchmark WHYSHIFT, we explore distribution shifts on 5 real-world tabular datasets from the economic and traffic sectors with natural spatiotemporal distribution shifts.We only pick 7 typical settings out of 22 settings and select only one representative target domain for each setting. In our benchmark, we specify the distribution shift pattern for each setting, and we provide the tools to identify risky regions with large $Y|X$ shifts and to diagnose the performance degradation.

1 PAPER • NO BENCHMARKS YET

fNIRS2MW (The Tufts fNIRS to Mental Workload Dataset)

The Tufts fNIRS to Mental Workload (fNIRS2MW) open-access dataset is a new dataset for building machine learning classifiers that can consume a short window (30 seconds) of multivariate fNIRS recordings and predict the mental workload intensity of the user during that window.

0 PAPER • NO BENCHMARKS YET

Datasets

25 dataset results for Domain Generalization