We introduce a new continual (or lifelong) learning algorithm called LDA-CP&S that performs segmentation tasks without undergoing catastrophic forgetting. The method is applied to two different surface defect segmentation problems that are learned incrementally, i.e. providing data about one type of defect at a time, while still being capable of predicting every defect that was seen previously. Our method creates a defect-related subnetwork for each defect type via iterative pruning and trains a classifier based on linear discriminant analysis (LDA). At the inference stage, we first predict the defect type with LDA and then predict the surface defects using the selected subnetwork. We compare our method with other continual learning methods showing a significant improvement -- mean Intersection over Union better by a factor of two when compared to existing methods on both datasets. Importantly, our approach shows comparable results with joint training when all the training data (all defects) are seen simultaneously
Inspired by Regularized Lottery Ticket Hypothesis (RLTH), which states that competitive smooth (non-binary) subnetworks exist within a dense network in continual learning tasks, we investigate two proposed architecture-based continual learning methods which sequentially learn and select adaptive binary- (WSN) and non-binary Soft-Subnetworks (SoftNet) for each task. WSN and SoftNet jointly learn the regularized model weights and task-adaptive non-binary masks of subnetworks associated with each task whilst attempting to select a small set of weights to be activated (winning ticket) by reusing w…
Delft University of Technology（代尔夫特理工大学）；Brown University（布朗大学）
The human brain is capable of learning tasks sequentially mostly without forgetting. However, deep neural networks (DNNs) suffer from catastrophic forgetting when learning one task after another. We address this challenge considering a class-incremental learning scenario where the DNN sees test data without knowing the task from which this data originates. During training, Continual-Prune-and-Select (CP&S) finds a subnetwork within the DNN that is responsible for solving a given task. Then, during inference, CP&S selects the correct subnetwork to make predictions for that task. A new task is l…
Evidence of an Emergent "Self" in Continual Robot Learning
Creative Machines Lab, Department of Mechanical Engineering, Columbia University（创意机器实验室，机械工程系，哥伦比亚大学）；Creative Machines Lab, Department of Computer Science, Columbia University（创意机器实验室，计算机科学系，哥伦比亚大学）
Out-of-distribution forgetting: vulnerability of continual learning to intra-class distribution shift
Institute of Automation, Chinese Academy of Sciences (CASIA)（中国科学院自动化研究所）；School of Future Technology, University of Chinese Academy of Sciences (UCAS)（中国科学院大学未来技术学院）；School of Artificial Intelligence, University of Chinese Academy of Sciences (UCAS)（中国科学院大学人工智能学院）
The feasibility of deep neural networks (DNNs) to address data stream problems still requires intensive study because of the static and offline nature of conventional deep learning approaches. A deep continual learning algorithm, namely autonomous deep learning (ADL), is proposed in this paper. Unlike traditional deep learning methods, ADL features a flexible structure where its network structure can be constructed from scratch with the absence of an initial network structure via the self-constructing network structure. ADL specifically addresses catastrophic forgetting by having a different-d…
SymbOmni: Evolving Agentic Omni Models via Symbolic Concept Learning
South China University of Technology（华南理工大学）；Westlake University（西湖大学）；Johns Hopkins University（约翰·霍普金斯大学）；The Chinese University of Hong Kong（香港中文大学）；Shenzhen Loop Area Institute（深圳河套学院）
Arctique: An artificial histopathological dataset unifying realism and controllability for uncertainty quantification
Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association（亥姆霍兹联合会马克斯·德尔布吕克分子医学中心）；Helmholtz Imaging（亥姆霍兹成像中心）；Charité—Universitätsmedizin Berlin（柏林夏里特医学院）；Freie Universität Berlin（柏林自由大学）；Humboldt-Universität zu Berlin（柏林洪堡大学）；University of Potsdam（波茨坦大学）
