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TABLE 2
Commonly used domain generalization datasets.
Benchmark # samples # domains Task Description
Rotated MNIST [53] 70,000 6 Handwritten digit recognition Rotation degree ∈ {0, 15, 30, 45, 60, 75}
Digits-DG [31] 24,000 4 Handwritten digit recognition Combination of MNIST [54], MNIST-M [13],
SVHN [55] and SYN [13]
VLCS [56] 10,729 4 Object recognition Combination of Caltech101 [39], LabelMe [40],
PASCAL [57], and SUN09 [58]
Office-31 [10] 4,652 3 Object recognition Domain ∈ {amazon, webcam, dslr}
OfficeHome [59] 15,588 4 Object recognition Domain ∈ {art, clipart, product, real}
PACS [33] 9,991 4 Object recognition Domain ∈ {photo, art, cartoon, sketch}
DomainNet [60] 586,575 6 Object recognition Domain ∈ {clipart, infograph, painting, quick-
draw, real, sketch}
miniDomainNet [61] 140,006 4 Object recognition A smaller and less noisy version of Domain-
Net; domain ∈ {clipart, painting, real, sketch}
ImageNet-Sketch [51] 50,000 2 Object recognition Domain shift between real and sketch images
VisDA-17 [62] 280,157 3 Object recognition Synthetic-to-real generalization
CIFAR-10-C [8] 60,000 - Object recognition
The test data are damaged by 15 corruptions
(each with 5 intensity levels) drawn from 4
categories (noise, blur, weather, and digital)
CIFAR-100-C [8] 60,000 - Object recognition
ImageNet-C [8] ≈1.3M - Object recognition
Visual Decathlon [63] 1,659,142 10 Object/action/handwritten
digit recognition
Combination of 10 datasets
IXMAS [64] 1,650 5 Action recognition 5 camera views; 10 subjects; 5 actions (see [27])
UCF-HMDB [65], [66] 3,809 2 Action recognition 12 overlapping actions (see [67])
SYNTHIA [68] 2,700 15 Semantic segmentation 4 locations; 5 weather conditions (see [43])
GTA5-Cityscapes [69], [70] 29,966 2 Semantic segmentation Synthetic-to-real generalization
TerraInc [71] 24,788 4 Animal classification Captured at different geographical locations
Market-Duke [72], [73] 69,079 2 Person re-identification Cross-dataset re-ID; heterogeneous DG
Face [36] >5M 9 Face recognition Combination of 9 face datasets
COMI [74], [75], [76], [77] ≈8,500 4 Face anti-spoofing Combination of 4 face anti-spoofing datasets
style is close to the target image style (both sharing the
same visual cues), the performance would be higher (e.g.,
photo→painting, both relying on colors and textures); oth-
erwise, if the source image style is drastically different from
the target image style, the performance would be poor
(e.g., photo→quickdraw, with the latter strongly relying on
shape information while requiring no color information at
all). This observation also applies to unsupervised domain
adaptation. For instance, the performance on the quickdraw
domain of DomainNet is usually the lowest among all target
domains [61], [81], [82].
3) A couple of recent DG studies [83], [84] have investi-
gated, from a transfer learning perspective, how to preserve
the knowledge learned via large-scale pre-training when
training on abundant labeled synthetic data for synthetic-
to-real applications. The experiments were carried out on
VisDA-17 [62]. This is an important yet under-studied topic
in DG: when only given sufficient synthetic data, how can
we avoid over-fitting in synthetic images by leveraging the
initialization weights learned on real images? Such a setting
is particularly useful to problems where manual labels are
difficult/expensive to obtain.
4) Synthetic image corruptions like Gaussian noise and
motion blur have also been used to simulate domain shift
by Hendrycks and Dietterich [8]. In their proposed datasets,
i.e. CIFAR-10-C, CIFAR-100-C and ImageNet-C, a model is
learned using the original images but tested on the cor-
rupted images. This research is largely motivated by adver-
sarial attacks [85], and aims to evaluate model robustness
under common image perturbations for safety applications.
5) Lastly, a hybrid dataset initially proposed for multi-
domain/task learning, i.e. Visual Decathlon [63], has also
been employed, for evaluating heterogeneous DG [34], [37].
However, due to both the changes in label space and the
use of target data for training SVM classifiers, this setup
overlaps with transfer learning [86].
Action Recognition Learning generalizable models is
critical for action recognition. This is because the test data
typically contain actions performed by new subjects in
new environments. IXMAS [64] has been widely used as
a cross-view action recognition benchmark [27], [37], which
contains action videos collected from five different views.
The common practice is to use four views for training and
the remaining view for test. In addition to view changes, dif-
ferent subjects and environments might also cause failure.
Intuitively, different persons can perform the same action in