Papers unsupervised class-incremental learning
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Exploiting Fine-Grained Prototype Distribution for Boosting Unsupervised Class Incremental Learning
The dynamic nature of open-world scenarios has attracted more attention to class incremental learning (CIL). However, existing CIL methods typically presume the availability of complete ground-truth labels throughout the…
class-incremental learningClass Incremental LearningIncremental Learningunsupervised class-incremental learningUCIL: An Unsupervised Class Incremental Learning Approach for Sound Event Detection
This work explores class-incremental learning (CIL) for sound event detection (SED), advancing adaptability towards real-world scenarios. CIL's success in domains like computer vision inspired our SED-tailored method, ad…
class-incremental learningClass Incremental LearningContinual LearningEvent Detection+3Neuro-mimetic Task-free Unsupervised Online Learning with Continual Self-Organizing Maps
An intelligent system capable of continual learning is one that can process and extract knowledge from potentially infinitely long streams of pattern vectors. The major challenge that makes crafting such a system difficu…
class-incremental learningClass Incremental LearningContinual LearningDimensionality Reduction+2Plasticity-Optimized Complementary Networks for Unsupervised Continual Learning
Continuous unsupervised representation learning (CURL) research has greatly benefited from improvements in self-supervised learning (SSL) techniques. As a result, existing CURL methods using SSL can learn high-quality re…
Continual LearningExemplar-FreeRepresentation LearningSelf-Supervised Learning+1Proxy Anchor-based Unsupervised Learning for Continuous Generalized Category Discovery
Recent advances in deep learning have significantly improved the performance of various computer vision applications. However, discovering novel categories in an incremental learning scenario remains a challenging proble…
class-incremental learningClass Incremental LearningIncremental Learningunsupervised class-incremental learningUnsupervised Class-Incremental Learning Through Confusion
While many works on Continual Learning have shown promising results for mitigating catastrophic forgetting, they have relied on supervised training. To successfully learn in a label-agnostic incremental setting, a model …
class-incremental learningClass Incremental LearningContinual Learningimage-classification+4