Dual Structural Knowledge Interaction for Domain Adaptation

An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision

“…These approaches exhibit significant promise in enhancing performance across various tasks and domains, as exemplified by [161], which introduces a multi-task learning framework for domain adaptation. Furthermore, certain models, such as [162], illustrate their potential by generating realistic target domain images, while [163] highlights the importance of learning invariant features for effective adaptation. Additionally, combining different techniques, as demonstrated in [164], with ensemble learning and self-supervised learning, can further contribute to improved domain adaptation performance.…”

“…Contribution Advantages [161] Introduces a new multi-task learning framework for domain adaptation Can improve performance on multiple tasks [162] Presents a model that works upon adversarial conditional image synthesis Can improve performance by generating realistic images from the target domain [167] Presents a model that works upon adversarial conditional variational autoencoders Can improve performance by using adversarial conditional variational autoencoders to learn representations that are invariant to domain shift [163] Introduces a new adversarial training framework for domain adaptation that uses an ensemble of discriminators Can improve performance by combining multiple discriminators [164] Presents a model that works upon adversarial training and meta-learning Can improve performance by using adversarial training and meta-learning to learn a model that can generalize to new domains [165] Introduces a new method for domain adaptation that combines adversarial training and self-supervised learning Can improve performance by learning features that are invariant to domain shift and using self-supervised learning to learn representations that are transferable to new domains [166] Introduces a new conditional generative adversarial network framework for domain adaptation Can improve performance by using conditional generative models [168] Presents a model that works upon conditional generative adversarial networks Can improve performance by using conditional generative adversarial networks to generate realistic images from the target domain [169] Presents a model that works upon conditional Wasserstein generative adversarial networks Can improve performance by generating realistic images from the target domain [170] Presents a model that works upon domain-invariant feature extraction Can improve performance by using domain-invariant feature extraction to learn features that are invariant to domain shift [171] Introduces a new method for domain adaptation that combines ensemble learning and self-supervised learning Can improve performance by combining multiple models and using self-supervised learning to learn features that are transferable to new domains [172] Presents a model that works upon ensemble learning and transfer learning Can improve performance by using ensemble learning and transfer learning to learn a model that can generalize to new domains [173] Presents a model that works upon few-shot learning and meta-learning Can improve performance by using few-shot learning to learn a model that can generalize to new domains and by using meta-learning to adapt to new domains more quickly [174] Presents a model that works upon few-shot learning and meta-learning Can improve performance by using few-shot learning and meta-learning to learn a model that can generalize to new domains Table 9. Cont.…”