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Domain generalization based on domain-specific adversarial learning
Deep learning models often suffer from degraded performance when the distributions of the training and testing data differ (i.e., domain shift)....
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Domain Generalization via Implicit Domain Augmentation
Deep convolutional neural networks often suffer significant performance degradation when deployed to an unknown domain. To tackle this problem,...
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Adversarial data splitting for domain generalization
Domain generalization aims to learn a model that is generalizable to an unseen target domain, which is a fundamental and challenging task in machine...
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Graph-based domain adversarial learning framework for video anomaly detection domain generalization
The limited domain generalization capability of contemporary video anomaly detection methods restricts their efficacy to specific datasets. To...
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Domain-Agnostic Priors for Semantic Segmentation Under Unsupervised Domain Adaptation and Domain Generalization
In computer vision, an important challenge to deep neural networks comes from adjusting the varying properties of different image domains. To study...
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Visual representations with texts domain generalization for semantic segmentation
At present, Domain generalization for semantic segmentation relying on deep neural networks has made little progress. Most of the current methods are...
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Joint Domain Alignment and Adversarial Learning for Domain Generalization
Domain generalization aims to extract a classifier model from multiple observed source domains, and then can be applied to unseen target domains. The...
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Quality-Invariant Domain Generalization for Face Anti-Spoofing
Face Anti-Spoofing (FAS) plays a critical role in safeguarding face recognition systems, while previous FAS methods suffer from poor generalization...
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Efficient Attention for Domain Generalization
Deep neural networks suffer severe performance degradation when encountering domain shift. Previous methods mainly focus on feature manipulation in...
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Cross-Domain Gated Learning for Domain Generalization
Domain generalization aims to improve the generalization capacity of a model by leveraging useful information from the multi-domain data. However,...
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Domain-Specific Bias Filtering for Single Labeled Domain Generalization
Conventional Domain Generalization (CDG) utilizes multiple labeled source datasets to train a generalizable model for unseen target domains. However,...
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MixStyle Neural Networks for Domain Generalization and Adaptation
Neural networks do not generalize well to unseen data with domain shifts—a longstanding problem in machine learning and AI. To overcome the problem,...
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Domain generalization for video anomaly detection considering diverse anomaly types
In intelligent video surveillance, anomaly detection is conducted to identify the occurrence of abnormal events by monitoring the video captured by...
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Learning Domain-Invariant Representations from Text for Domain Generalization
Domain generalization (DG) aims to transfer the knowledge learned in the source domain to the unseen target domain. Most DG methods focus on studying...
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Detecting facial manipulated images via one-class domain generalization
Nowadays, numerous synthesized images and videos generated by facial manipulated techniques have become an emerging problem, which promotes facial...
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CSDG-FAS: Closed-Space Domain Generalization for Face Anti-spoofing
Domain generalization based Face Anti-spoofing (FAS) aims to enhance its ability to work in unseen domains. Existing methods endeavor to extract a...
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Domain Generalization with Small Data
In this work, we propose to tackle the problem of domain generalization in the context of insufficient samples . Instead of extracting latent feature...
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Semi-Supervised Domain Generalization with Stochastic StyleMatch
Ideally, visual learning algorithms should be generalizable, for dealing with any unseen domain shift when deployed in a new target environment; and...
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Domain adversarial neural networks for domain generalization: when it works and how to improve
Theoretically, domain adaptation is a well-researched problem. Further, this theory has been well-used in practice. In particular, we note the bound...
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Multi-source domain generalization peron re-identification with knowledge accumulation and distribution enhancement
Domain generalization person re-identification (re-ID) is a more realistic task that aims to learn a model with multiple labeled source domains and...
