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Found 8 papers, 6 papers with code
MIND: Multi-Task Incremental Network Distillation
The recent surge of pervasive devices that generate dynamic data streams has underscored the necessity for learning systems to adapt continually to data distributional shifts.
DUCK: Distance-based Unlearning via Centroid Kinematics
Machine Unlearning is rising as a new field, driven by the pressing necessity of ensuring privacy in modern artificial intelligence models.
Dissecting Continual Learning a Structural and Data Analysis
At the end we conclude with a study on pretraining and how it affects the performance in Continual Learning, raising some questions about the effective progression of the field.
Simpler is Better: off-the-shelf Continual Learning Through Pretrained Backbones
In this short paper, we propose a baseline (off-the-shelf) for Continual Learning of Computer Vision problems, by leveraging the power of pretrained models.
Towards Exemplar-Free Continual Learning in Vision Transformers: an Account of Attention, Functional and Weight Regularization
In this paper, we investigate the continual learning of Vision Transformers (ViT) for the challenging exemplar-free scenario, with special focus on how to efficiently distill the knowledge of its crucial self-attention mechanism (SAM).
Smaller Is Better: An Analysis of Instance Quantity/Quality Trade-off in Rehearsal-based Continual Learning
The design of machines and algorithms capable of learning in a dynamically changing environment has become an increasingly topical problem with the increase of the size and heterogeneity of data available to learning systems.
Unsupervised semantic discovery through visual patterns detection
We propose a new fast fully unsupervised method to discover semantic patterns.
Graph Compression Using The Regularity Method
We are living in a world which is getting more and more interconnected and, as physiological effect, the interaction between the entities produces more and more information.
Data Structures and Algorithms 68R10
