122 research outputs found
Hallucinating Agnostic Images to Generalize Across Domains
The ability to generalize across visual domains is crucial for the robustness
of artificial recognition systems. Although many training sources may be
available in real contexts, the access to even unlabeled target samples cannot
be taken for granted, which makes standard unsupervised domain adaptation
methods inapplicable in the wild. In this work we investigate how to exploit
multiple sources by hallucinating a deep visual domain composed of images,
possibly unrealistic, able to maintain categorical knowledge while discarding
specific source styles. The produced agnostic images are the result of a deep
architecture that applies pixel adaptation on the original source data guided
by two adversarial domain classifier branches at image and feature level. Our
approach is conceived to learn only from source data, but it seamlessly extends
to the use of unlabeled target samples. Remarkable results for both
multi-source domain adaptation and domain generalization support the power of
hallucinating agnostic images in this framework
DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentation
While deep learning demonstrates its strong ability to handle independent and
identically distributed (IID) data, it often suffers from out-of-distribution
(OoD) generalization, where the test data come from another distribution
(w.r.t. the training one). Designing a general OoD generalization framework to
a wide range of applications is challenging, mainly due to possible correlation
shift and diversity shift in the real world. Most of the previous approaches
can only solve one specific distribution shift, such as shift across domains or
the extrapolation of correlation. To address that, we propose DecAug, a novel
decomposed feature representation and semantic augmentation approach for OoD
generalization. DecAug disentangles the category-related and context-related
features. Category-related features contain causal information of the target
object, while context-related features describe the attributes, styles,
backgrounds, or scenes, causing distribution shifts between training and test
data. The decomposition is achieved by orthogonalizing the two gradients
(w.r.t. intermediate features) of losses for predicting category and context
labels. Furthermore, we perform gradient-based augmentation on context-related
features to improve the robustness of the learned representations. Experimental
results show that DecAug outperforms other state-of-the-art methods on various
OoD datasets, which is among the very few methods that can deal with different
types of OoD generalization challenges.Comment: Accepted by AAAI202
Encouraging Intra-Class Diversity Through a Reverse Contrastive Loss for Better Single-Source Domain Generalization
Traditional deep learning algorithms often fail to generalize when they are
tested outside of the domain of training data. Because data distributions can
change dynamically in real-life applications once a learned model is deployed,
in this paper we are interested in single-source domain generalization (SDG)
which aims to develop deep learning algorithms able to generalize from a single
training domain where no information about the test domain is available at
training time. Firstly, we design two simple MNISTbased SDG benchmarks, namely
MNIST Color SDG-MP and MNIST Color SDG-UP, which highlight the two different
fundamental SDG issues of increasing difficulties: 1) a class-correlated
pattern in the training domain is missing (SDG-MP), or 2) uncorrelated with the
class (SDG-UP), in the testing data domain. This is in sharp contrast with the
current domain generalization (DG) benchmarks which mix up different
correlation and variation factors and thereby make hard to disentangle success
or failure factors when benchmarking DG algorithms. We further evaluate several
state-of-the-art SDG algorithms through our simple benchmark, namely MNIST
Color SDG-MP, and show that the issue SDG-MP is largely unsolved despite of a
decade of efforts in developing DG algorithms. Finally, we also propose a
partially reversed contrastive loss to encourage intra-class diversity and find
less strongly correlated patterns, to deal with SDG-MP and show that the
proposed approach is very effective on our MNIST Color SDG-MP benchmark
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