Subsequently, through feature fusion plus classifier, the forgetting can be effectively countered, and adaptation to the emergence of new classes can be achieved. For the first time, Kalla and Biswas (2022) proposed the self-supervised stochastic classifier (S3C) to solve FSCIL. The stochasticity ...
We introduce a practical Domain Adaptation (DA) paradigm called Class-Incremental Domain Adaptation (CIDA). Existing DA methods tackle domain-shift but are unsuitable for learning novel target-domain classes. Meanwhile, class-incremental (CI) methods enable learning of new classes in absence of ...
Class- incremental unsupervised domain adaptation via pseudo- label distillation. IEEE Trans. Image Process., 2024. 1 [41] Jiexi Yan, Lei Luo, Cheng Deng, and Heng Huang. Adap- tive hierarchical similarity metric learning with noisy labels. IEEE Trans. Image...
This can be attributed to PL-FSCIL’s use of a pre-trained ViT model and the Domain Prompt, which together enhance domain adaptation for new datasets. To validate the Domain Prompt’s efficacy, we select four classic classification datasets: CIFAR-10 [45], STL-10 [58], Flowers-102 [59...
Where and how to transfer: Knowledge aggregation-induced transferability perception for unsuper- vised domain adaptation. IEEE Transactions on Pattern Anal- ysis and Machine Intelligence, pages 11–20, 2021. [12] Jiahua Dong, Lixu Wang, Zhen Fang, Gan Sun, Shichao Xu...
Unlike traditional supervised machine learning approaches that require a large number of examples for each class and therefore only adapt to new classes slowly, few-shot class-incremental learning utilizes techniques such as meta-learning, knowledge distillation, domain adaptation, and cross-task learning...
Componential Prompt-Knowledge Alignment for Domain Incremental LearningarXiv 2505.04575元素丰富,配色简单,整体丰满结构清晰,布局协调,适合模仿学习看完了还不会怎么办?别担心,学姐还可以帮你一对一辅导,风里雨里学姐一路陪你!#domain adaptation #深度学习在搜索和推荐中的应用 #营地规划 #Domain-Driven Design in...
IL has emerged as a promising approach for developing dynamic adaptation capabilities for novel classes or tasks. However, previous works have not explored the application of IL techniques to continuously analyze histological images. The need for lifelong models in the medical domain is crucial, as ...
Class-Incremental Domain Adaptation We introduce a practical Domain Adaptation (DA) paradigm called Class-Incremental Domain Adaptation (CIDA). Existing DA methods tackle domain-shift but are unsuitable for learning novel target-domain classes. Meanwhile, class-incremental (CI) methods enable learning of...
We introduce a practical Domain Adaptation (DA) paradigm called Class-Incremental Domain Adaptation (CIDA). Existing DA methods tackle domain-shift but are unsuitable for learning novel target-domain classes. Meanwhile, class-incremental (CI) methods enable learning of new classes in absence of ...