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Catastrophic forgetting: still a problem for DNNs
, 2019 We investigate the performance of DNNs when trained on class-incremental visual problems consisting of initial training, followed by retraining with added visual classes.
Abdullah, S. +3 more
core +1 more source
Privacy-Preserving Federated Class-Incremental Learning
IEEE Transactions on Machine Learning in Communications and NetworkingFederated Learning (FL) offers a collaborative training framework, aggregating model parameters from decentralized clients. Many existing models, however, assume static, predetermined data classes within FLa frequently unrealistic assumption.
Jue Xiao, Xueming Tang, Songfeng Lu
doaj +1 more source
Incremental multiclass open-set audio recognition
IJAIN (International Journal of Advances in Intelligent Informatics), 2022 Incremental learning aims to learn new classes if they emerge while maintaining the performance for previously known classes. It acquires useful information from incoming data to update the existing models.
Hitham Jleed, Martin Bouchard
doaj +1 more source
Long-Tailed Class Incremental Learning
, 2022 In class incremental learning (CIL) a model must learn new classes in a sequential manner without forgetting old ones. However, conventional CIL methods consider a balanced distribution for each new task, which ignores the prevalence of long-tailed distributions in the real world.
Liu, Xialei +5 more
openaire +3 more sources
On OWA, Machine Learning and Big Data: The case for IFS over universes [PDF]
Notes on IFSThis paper provides a holistic view of open-world machine learning by investigating class discovery, and class incremental learning under OWA. The challenges, principles, and limitations of current methodologies are discussed in detail.
Panagiotis Chountas +2 more
doaj +1 more source
, 2017 It is often desirable to be able to recognize when inputs to a recognition function learned in a supervised manner correspond to classes unseen at training time.
Boult, Terrance E. +3 more
core +1 more source
Direct kernel biased discriminant analysis: a new content-based image retrieval relevance feedback algorithm [PDF]
, 2006 In recent years, a variety of relevance feedback (RF) schemes have been developed to improve the performance of content-based image retrieval (CBIR). Given user feedback information, the key to a RF scheme is how to select a subset of image features to ...
Dacheng Tao +6 more
core +2 more sources
Semantic Drift Compensation for Class-Incremental Learning [PDF]
Computer Vision and Pattern Recognition, 2020 Class-incremental learning of deep networks sequentially increases the number of classes to be classified. During training, the network has only access to data of one task at a time, where each task contains several classes.
Lu Yu +7 more
semanticscholar +1 more source
A Mahalanobis Hyperellipsoidal Learning Machine Class Incremental Learning Algorithm
Abstract and Applied Analysis, 2014 A Mahalanobis hyperellipsoidal learning machine class incremental learning algorithm is proposed. To each class sample, the hyperellipsoidal that encloses as many as possible and pushes the outlier samples away is trained in the feature space.
Yuping Qin +4 more
doaj +1 more source
Balanced Contrast Class‐Incremental Learning
CAAI Transactions on Intelligence TechnologyContinual learning aims to empower a model to learn new tasks continuously while reducing forgetting to retain previously learnt knowledge. In the context of receiving streaming data that are not constrained by the independent and identically distributed
Shiqi Yu, Luojun Lin, Yuanlong Yu
doaj +1 more source