Class-Incremental Domain Adaptation 类增长域自适应 摘要: 引入了CIDA范例,现存的DA方法能解决domain-shift问题但是不使用在学习目标域中新颖的类别,CI方法在源训练数据缺失的情况下可以学习新的类别,但是不能解决无监督的domain-shift问题,本文就是解决CIDA问题,基于原型网络可以识别shared-class和novel class(one-...
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 ...
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 ...
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...
Hence, we propose a 2-stage training scheme for CIL starting with a large number of base classes: i) duplicat- ing part of the backbone as the adaptation module and fine- tuning it on the novel data, and ii) combining all the inde- pendently trained base and novel ...
Unsupervised Domain Adaptation Through Self-Supervision论文阅读笔记 这篇文章是arxiv上的一篇domian adaptation的文章,觉得这里使用的方法和之前的方法完全不同,很有意思。所以整理了一下,和大家一起分享。 本文讨论了无监督域自适应,即源域上有标记的训练数据,但目标是在目标域上只有未标记数据,希望能够通过一些方法...
Stochastic classifiers for unsupervised domain adaptation 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020), pp. 9108-9117 CrossrefView in ScopusGoogle Scholar [9] A. Hassani, S. Walton, N. Shah, A. Abuduweili, J. Li, H. Shi Escaping the big data paradigm...
We address the problem of class incremental learning, which is a core step towards achieving adaptive vision intelligence. In particular, we consider the task setting of incremental learning with limited memory and aim to achieve better stability-plasticity trade-off. To this end, we propose a nov...
GPNN qi2018learningInteractionFew---0.29160.2426-0.33990.2271- Islam et al. islam2020learningInteractionUDA---0.48020.2157-0.31440.1996- Islam et al. islam2020learningInteractionFew---0.27650.1913-0.39750.2086- VS-GAT liang2021visualInteractionUDA---0.63050.26580.28680.33190.07770.0949 VS-GAT...
In this work, we propose\nclass-incremental domain adaptation (CIDA) with a multi-layer transformer-based\nmodel to tackle the new classes and domain shift in the target domain to\ngenerate surgical reports during robotic surgery. To adapt incremental classes\nand extract domain invariant features...