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Found 11 papers, 8 papers with code
Dark Experience for General Continual Learning: a Strong, Simple Baseline
Continual Learning has inspired a plethora of approaches and evaluation settings; however, the majority of them overlooks the properties of a practical scenario, where the data stream cannot be shaped as a sequence of tasks and offline training is not viable.
Ranked #12 on
Continual Learning
on ASC (19 tasks)
Rethinking Experience Replay: a Bag of Tricks for Continual Learning
In Continual Learning, a Neural Network is trained on a stream of data whose distribution shifts over time.
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.
Class-Incremental Continual Learning into the eXtended DER-verse
The staple of human intelligence is the capability of acquiring knowledge in a continuous fashion.
Transfer without Forgetting
This work investigates the entanglement between Continual Learning (CL) and Transfer Learning (TL).
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.
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.
Latent Spectral Regularization for Continual Learning
While biological intelligence grows organically as new knowledge is gathered throughout life, Artificial Neural Networks forget catastrophically whenever they face a changing training data distribution.
On the Effectiveness of Equivariant Regularization for Robust Online Continual Learning
Humans can learn incrementally, whereas neural networks forget previously acquired information catastrophically.
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).
Selective Attention-based Modulation for Continual Learning
We present SAM, a biologically-plausible selective attention-driven modulation approach to enhance classification models in a continual learning setting.
