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 ...
This is the official code for FeTrIL (WACV2023): Feature Translation for Exemplar-Free Class-Incremental Learning Abstract Exemplar-free class-incremental learning is very challenging due to the negative effect of catastrophic forgetting. A balance between stability and plasticity of the incremental proc...
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 ...
which demonstrated that a combination of the three components is necessary. We also compared the performance of the proposed method with that of other widely used incremental learning methods (see Methods, Supplementary Table3). The exemplar-based method in EPicker exhibited the best stability on ...
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 ...
SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning Sungmin Cha1,2*, Beomyoung Kim3*, YoungJoon Yoo2,3, Taesup Moon1 * Equal contribution 1Department of Electrical and Computer Engineering, Seoul National University ...
we propose a novel class incremental learning method calledExemplar-Supported Generative Reproduction (ESGR)that can better reconstruct memory of old classes and mitigate catastrophic forgetting. Specifically, we use Generative Adversarial Networks (GANs) to model the underlying distributions of old classes ...
Exemplar-free class-incremental learning using a backbone trained from scratch and starting from a small first task presents a significant challenge for continual representation learning. Prototype-based approaches, when continually updated, face the critical issue of semantic drift due to which the old...