3,501 research outputs found
Using Hindsight to Anchor Past Knowledge in Continual Learning
In continual learning, the learner faces a stream of data whose distribution
changes over time. Modern neural networks are known to suffer under this
setting, as they quickly forget previously acquired knowledge. To address such
catastrophic forgetting, many continual learning methods implement different
types of experience replay, re-learning on past data stored in a small buffer
known as episodic memory. In this work, we complement experience replay with a
new objective that we call anchoring, where the learner uses bilevel
optimization to update its knowledge on the current task, while keeping intact
the predictions on some anchor points of past tasks. These anchor points are
learned using gradient-based optimization to maximize forgetting, which is
approximated by fine-tuning the currently trained model on the episodic memory
of past tasks. Experiments on several supervised learning benchmarks for
continual learning demonstrate that our approach improves the standard
experience replay in terms of both accuracy and forgetting metrics and for
various sizes of episodic memories.Comment: Accepted at AAAI 202
Hindsight Bias And The Evaluation Of Strategic Performance
This article reviews the literature on hindsight bias and applies it to the context of strategic performance evaluation. Hindsight bias, the tendency for people to view an event as more foreseeable after the event than prior to the event, is a well-documented cognitive bias. In evaluating the quality of the processes by which strategic decisions are made, evaluators are aware of the outcomes of these decisions and, therefore, are subject to the distorting effects of hindsight bias. Preventative measures are reviewed and recommendations for further research suggested
Towards Robust Feature Learning with t-vFM Similarity for Continual Learning
Continual learning has been developed using standard supervised contrastive
loss from the perspective of feature learning. Due to the data imbalance during
the training, there are still challenges in learning better representations. In
this work, we suggest using a different similarity metric instead of cosine
similarity in supervised contrastive loss in order to learn more robust
representations. We validate the our method on one of the image classification
datasets Seq-CIFAR-10 and the results outperform recent continual learning
baselines
Continual semi-supervised learning through contrastive interpolation consistency
Continual Learning (CL) investigates how to train Deep Networks on a stream of tasks without incurring forgetting. CL settings proposed in literature assume that every incoming example is paired with ground-truth annotations. However, this clashes with many real-world applications: gathering labeled data, which is in itself tedious and expensive, becomes infeasible when data flow as a stream. This work explores Continual Semi-Supervised Learning (CSSL): here, only a small fraction of labeled input examples are shown to the learner. We assess how current CL methods (e.g.: EWC, LwF, iCaRL, ER, GDumb, DER) perform in this novel and challenging scenario, where overfitting entangles forgetting. Subsequently, we design a novel CSSL method that exploits metric learning and consistency regularization to leverage unlabeled examples while learning. We show that our proposal exhibits higher resilience to diminishing supervision and, even more surprisingly, relying only on supervision suffices to outperform SOTA methods trained under full supervision
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