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Found 8 papers, 5 papers with code
Semantic Residual Prompts for Continual Learning
Most of these methods organize these vectors in a pool of key-value pairs, and use the input image as query to retrieve the prompts (values).
On the Effectiveness of Equivariant Regularization for Robust Online Continual Learning
Humans can learn incrementally, whereas neural networks forget previously acquired information catastrophically.
On the Effectiveness of Lipschitz-Driven Rehearsal in Continual Learning
By means of extensive experiments, we show that applying LiDER delivers a stable performance gain to several state-of-the-art rehearsal CL methods across multiple datasets, both in the presence and absence of pre-training.
Spotting Virus from Satellites: Modeling the Circulation of West Nile Virus Through Graph Neural Networks
In this paper, we seek to predict WNV circulation by feeding Deep Neural Networks (DNNs) with satellite images, which have been extensively shown to hold environmental and climatic features.
Effects of Auxiliary Knowledge on Continual Learning
In this paper we propose a new, simple, CL algorithm that focuses on solving the current task in a way that might facilitate the learning of the next ones.
Transfer without Forgetting
This work investigates the entanglement between Continual Learning (CL) and Transfer Learning (TL).
Class-Incremental Continual Learning into the eXtended DER-verse
The staple of human intelligence is the capability of acquiring knowledge in a continuous fashion.
Continual Semi-Supervised Learning through Contrastive Interpolation Consistency
This work explores Continual Semi-Supervised Learning (CSSL): here, only a small fraction of labeled input examples are shown to the learner.
