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Meta-learning for real-world class incremental learning: a transformer-based approach [PDF]
Scientific ReportsModern natural language processing (NLP) state-of-the-art (SoTA) deep learning (DL) models have hundreds of millions of parameters, making them extremely complex.
Sandeep Kumar +5 more
doaj +4 more sources
Few-Shot Class-Incremental Learning [PDF]
Computer Vision and Pattern Recognition, 2020 The ability to incrementally learn new classes is crucial to the development of real-world artificial intelligence systems. In this paper, we focus on a challenging but practical few-shot class-incremental learning (FSCIL) problem.
Xiaoyu Tao +5 more
openalex +3 more sources
Class incremental learning of remote sensing images based on class similarity distillation [PDF]
PeerJ Computer Science, 2023 When a well-trained model learns a new class, the data distribution differences between the new and old classes inevitably cause catastrophic forgetting in order to perform better in the new class. This behavior differs from human learning.
Mingge Shen +3 more
doaj +3 more sources
FeTT: Class-Incremental Learning with Feature Transformation Tuning [PDF]
MathematicsClass-incremental learning (CIL) enables models to continuously acquire knowledge and adapt in an ever-changing environment. However, one primary challenge lies in the trade-off between the stability and plasticity, i.e., plastically expand the novel ...
Sunyuan Qiang, Yanyan Liang
doaj +2 more sources
PS-SNN: pattern separation learning for expandable spiking neural networks in class-incremental learning [PDF]
Scientific ReportsBiological brains mitigate interference by orthogonalizing neural representations of similar memories, thereby preserving stability across tasks in continual learning. However, most existing continual learning approaches for spiking neural networks (SNNs)
Ke Hu +3 more
doaj +2 more sources
IEEE AccessDefect classification for thin-film transistor liquid crystal displays (TFT-LCD) poses significant challenges due to the fine-grained nature of microscopic defects and the limited availability of labeled data.
Anindita Suryarasmi +4 more
doaj +2 more sources
CAREC: Continual Wireless Action Recognition with Expansion–Compression Coordination [PDF]
SensorsIn real-world applications, user demands for new functionalities and activities constantly evolve, requiring action recognition systems to incrementally incorporate new action classes without retraining from scratch. This class-incremental learning (CIL)
Tingting Zhang +4 more
doaj +2 more sources
DER: Dynamically Expandable Representation for Class Incremental Learning [PDF]
Computer Vision and Pattern Recognition, 2021 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 ...
Shipeng Yan, Jiangwei Xie, Xuming He
semanticscholar +1 more source
Forward Compatible Few-Shot Class-Incremental Learning [PDF]
Computer Vision and Pattern Recognition, 2022 Novel classes frequently arise in our dynamically changing world, e.g., new users in the authentication system, and a machine learning model should recognize new classes without forgetting old ones.
Da-Wei Zhou +5 more
semanticscholar +1 more source
Revisiting Class-Incremental Learning with Pre-Trained Models: Generalizability and Adaptivity are All You Need [PDF]
International Journal of Computer Vision, 2023 Class-incremental learning (CIL) aims to adapt to emerging new classes without forgetting old ones. Traditional CIL models are trained from scratch to continually acquire knowledge as data evolves. Recently, pre-training has achieved substantial progress,
Da-Wei Zhou +3 more
semanticscholar +1 more source