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Domain generalization: A survey
Generalization to out-of-distribution (OOD) data is a capability natural to humans yet
challenging for machines to reproduce. This is because most learning algorithms strongly …
challenging for machines to reproduce. This is because most learning algorithms strongly …
Enhancing deep reinforcement learning: A tutorial on generative diffusion models in network optimization
Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of
Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy across …
Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy across …
A dual-augmentor framework for domain generalization in 3d human pose estimation
Abstract 3D human pose data collected in controlled laboratory settings present challenges
for pose estimators that generalize across diverse scenarios. To address this domain …
for pose estimators that generalize across diverse scenarios. To address this domain …
Sharpness-aware gradient matching for domain generalization
The goal of domain generalization (DG) is to enhance the generalization capability of the
model learned from a source domain to other unseen domains. The recently developed …
model learned from a source domain to other unseen domains. The recently developed …
Towards out-of-distribution generalization: A survey
Traditional machine learning paradigms are based on the assumption that both training and
test data follow the same statistical pattern, which is mathematically referred to as …
test data follow the same statistical pattern, which is mathematically referred to as …
Federated learning for generalization, robustness, fairness: A survey and benchmark
Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …
collaboration among different parties. Recently, with the popularity of federated learning, an …
Exact feature distribution matching for arbitrary style transfer and domain generalization
Arbitrary style transfer (AST) and domain generalization (DG) are important yet challenging
visual learning tasks, which can be cast as a feature distribution matching problem. With the …
visual learning tasks, which can be cast as a feature distribution matching problem. With the …
Generalizing to unseen domains: A survey on domain generalization
Machine learning systems generally assume that the training and testing distributions are
the same. To this end, a key requirement is to develop models that can generalize to unseen …
the same. To this end, a key requirement is to develop models that can generalize to unseen …
Clip the gap: A single domain generalization approach for object detection
Abstract Single Domain Generalization (SDG) tackles the problem of training a model on a
single source domain so that it generalizes to any unseen target domain. While this has …
single source domain so that it generalizes to any unseen target domain. While this has …
Swad: Domain generalization by seeking flat minima
Abstract Domain generalization (DG) methods aim to achieve generalizability to an unseen
target domain by using only training data from the source domains. Although a variety of DG …
target domain by using only training data from the source domains. Although a variety of DG …