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 ...
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 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...
Class-Incremental Learning refers to the process of learning in real-world scenarios where data is limited and new learning data is continuously presented. It involves categorizing approaches into different families to balance performance, scalability, efficiency, and complexity in the field of deep lear...
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...
Using the learner itself to handle skewed distribution, which is another algorithmic method used in the FDS literature. These learners are either resistant to the class imbalance problem through inherent properties of the learner, as in the case of the Repeated Incremental Pruning to Produce Error ...
few-shot class-incremental learning Few-shot class-incremental learning is a form of machine learning that focuses on the ability to teach a model to generalize from a limited number of examples and then continually and incrementally adapt to new classesof data without catastrophic forgetting. This...
” This component of the loss function has two phases: for the first part of training, it is a cross-entropy loss which balances learning new information with retaining old information (a common approach in class-incremental learning); for the second part, a rebalancing component is added. ...
few shot learning; incremental learning; meta-learning; feature replay; prototype calibration1. Introduction Compared with artificial intelligence, human intelligence possesses a unique ability to learn from only a small amount of data, relying on its special memorizing and reasoning capabilities [1]. ...
They leverage on incremental learning and support vector machine to classify what features may lead to accurate diagnosis of the disease. In recent study, Bhattacharya and Lane (2016) investigated smartwatch-centric activity recognition and the possibility of implementing deep learning in wearable ...