Class incremental learningFault diagnosisRotating machineryData privacyA new adaptive prototype correction and separation network is proposed for example-free incremental fault diagnosis.Prototype correction mo
We introduce EFCIA, a new action recognition method which addresses exemplar-free class-incremental learning. Specifically, we propose the use of a self-supervised model and knowledge distillation to enhance the feature extraction capability of the feature extractor. The classifier trained by pseudo-fea...
Exemplar-free class-incremental learning (CIL) poses several challenges since it prohibits the rehearsal of data from previous tasks and thus suffers from catastrophic forgetting. Recent approaches to incrementally learning the classifier by freezing the feature extractor after the first task have gained ...
This paper proposes a new exemplar-free approach for class/task incremental learning called ConTraCon, which does not require task-id to be explicitly present during in- ference and avoids the need for storing previous training instances. The proposed approach leverages the transformer archit...
SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning Sungmin Cha1,2*, Beomyoung Kim3*, YoungJoon Yoo2,3, Taesup Moon1 * Equal contribution 1 Department of Electrical and Computer Engineering, Seoul National University 2 NAVER AI Lab 3 Face, NAVER Clova...
The exemplar-free class incremental learning requires classification models to learn new class knowledge incrementally without retaining any old samples. Recently, the framework based on parallel one-class classifiers (POC), which trains a one-class classifier (OCC) independently for each category, has...
Exemplar-free Class Incremental Learning via Discriminative and Comparable One-class Classifiers SunWenJu123/DCPOC • • 5 Jan 2022 DisCOIL follows the basic principle of POC, but it adopts variational auto-encoders (VAE) instead of other well-established one-class classifiers (e. g. deep ...
Unlike exemplar-based class-incremental learning (EBCIL) which allows storing some old samples, exemplar-free class-incremental learning (EFCIL) faces a more severe forgetting problem due to the complete prohibition on accessing old data. Some previous methods freeze the feature extractor after the ...
Extensive experiments on five datasets with different settings demonstrate the superiority of our method against the state-of-the-art exemplar-free class-incremental learning methods.Heng Tianhttps://ror.org/01mv9t934grid.419897.a0000 0004 0369 313XKey Laboratory of Smart Manufacturing in Energy ...
Exemplar-free class-incremental learning (EFCIL) presents a significant challenge as the old class samples are absent for new task learning. Due to the severe imbalance between old and new class samples, the learned classifiers can be easily biased toward the new ones. Moreover, continually ...