Results 31 to 40 of about 842,651 (374)
Class-Incremental Learning of Convolutional Neural Networks Based on Double Consolidation Mechanism
IEEE Access, 2020 Class-incremental learning is a model learning technique that can help classification models incrementally learn about new target classes and realize knowledge accumulation.
Leilei Jin, Hong Liang, Changsheng Yang
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Class-Incremental Learning Based on Anomaly Detection
IEEE Access, 2023 In Class-Incremental Learning (CIL), the memoryless replay CIL algorithm trains the network model by using the closed training set of the current task, while using knowledge distillation or finetuning constraints to keep the old knowledge.
Lijuan Zhang +6 more
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Detachedly Learn a Classifier for Class-Incremental Learning
CoRR, 2023 In continual learning, model needs to continually learn a feature extractor and classifier on a sequence of tasks. This paper focuses on how to learn a classifier based on a pretrained feature extractor under continual learning setting. We present an probabilistic analysis that the failure of vanilla experience replay (ER) comes from unnecessary re ...
Ziheng Li, Shibo Jie, Zhi-Hong Deng 0001
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Co-Transport for Class-Incremental Learning [PDF]
Proceedings of the 29th ACM International Conference on Multimedia, 2021 Accepted to ACM Multimedia ...
Da-Wei Zhou 0001 +2 more
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Few-Shot Class-Incremental Learning from an Open-Set Perspective [PDF]
European Conference on Computer Vision, 2022 The continual appearance of new objects in the visual world poses considerable challenges for current deep learning methods in real-world deployments. The challenge of new task learning is often exacerbated by the scarcity of data for the new categories ...
Can Peng +4 more
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Learning with Fantasy: Semantic-Aware Virtual Contrastive Constraint for Few-Shot Class-Incremental Learning [PDF]
Computer Vision and Pattern Recognition, 2023 Few-shot class-incremental learning (FSCIL) aims at learning to classify new classes continually from limited samples without forgetting the old classes.
Zeyin Song +5 more
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New Generation Federated Learning
Sensors, 2022 With the development of the Internet of things (IoT), federated learning (FL) has received increasing attention as a distributed machine learning (ML) framework that does not require data exchange. However, current FL frameworks follow an idealized setup
Boyuan Li, Shengbo Chen, Zihao Peng
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Audio-Visual Class-Incremental Learning
2023 IEEE/CVF International Conference on Computer Vision (ICCV), 2023 In this paper, we introduce audio-visual class-incremental learning, a class-incremental learning scenario for audio-visual video recognition. We demonstrate that joint audio-visual modeling can improve class-incremental learning, but current methods fail to preserve semantic similarity between audio and visual features as incremental step grows ...
Weiguo Pian +3 more
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Class-Incremental Learning Based on Big Dataset Pre-Trained Models
IEEE Access, 2023 Deep neural networks have shown excellent performance in the field of pattern classification and are widely used. However, real-world data are often cannot be obtained at once, and the knowledge of old classes will be heavily forgotten when training new ...
Bin Wen, Qiuyu Zhu
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Hierarchical incremental class learning with reduced pattern training [PDF]
, 2006 Hierarchical Incremental Class Learning (HICL) is a new task decomposition method that addresses the pattern classification problem. HICL is proven to be a good classifier but closer examination reveals areas for potential improvement.
Bao, C, Guan, SU, Sun, RT
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