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Incremental Learning of Object Detectors without Catastrophic Forgetting [PDF]
, 2017 Despite their success for object detection, convolutional neural networks are ill-equipped for incremental learning, i.e., adapting the original model trained on a set of classes to additionally detect objects of new classes, in the absence of the ...
Alahari, Karteek +2 more
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Incremental learning with respect to new incoming input attributes [PDF]
, 2001 Neural networks are generally exposed to a dynamic environment where the training patterns or the input attributes (features) will likely be introduced into the current domain incrementally.
Guan, SU, Li, SC
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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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Incremental Object Detection Inspired by Memory Mechanisms in Brain [PDF]
Jisuanji kexue, 2023 Incremental learning is key to bridging the enormous gap between artificial intelligence and human intelligence,mea-ning that agents can learn several tasks sequentially from a continuous stream of correlated data without forgetting,just as humans do ...
SHANG Di, LYU Yanfeng, QIAO Hong
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Models of incremental concept formation [PDF]
, 1988 Given a set of observations, humans acquire concepts that organize those observations and use them in classifying future experiences. This type of concept formation can occur in the absence of a tutor and it can take place despite irrelevant and ...
Fisher, Douglas +2 more
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Confidence Calibration for Incremental Learning
IEEE Access, 2020 Class incremental learning is an online learning paradigm wherein the classes to be recognized are gradually increased with limited memory, storing only a partial set of examples of past tasks.
Dongmin Kang +3 more
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Learning Entropy: Multiscale Measure for Incremental Learning
Entropy, 2013 First, this paper recalls a recently introduced method of adaptive monitoring of dynamical systems and presents the most recent extension with a multiscale-enhanced approach.
Ivo Bukovsky
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Incremental Learning of Latent Forests
IEEE Access, 2020 In the analysis of real-world data, it is useful to learn a latent variable model that represents the data generation process. In this setting, latent tree models are useful because they are able to capture complex relationships while being easily ...
Fernando Rodriguez-Sanchez +2 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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Chunk Incremental Canonical Correlation Analysis [PDF]
Jisuanji kexue yu tansuo, 2022 For the large-scale dynamic data stream, incremental learning is an effective and efficient technique and is widely used in machine learning. Incremental dimensionality reduction algorithms have been proposed by many scholars.
PAN Yu, CHEN Xiaohong, LI Shunming, LI Jiyong
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