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Found 10 papers, 7 papers with code
Towards Brain Inspired Design for Addressing the Shortcomings of ANNs
As our understanding of the mechanisms of brain function is enhanced, the value of insights gained from neuroscience to the development of AI algorithms deserves further consideration.
A Study of Biologically Plausible Neural Network: The Role and Interactions of Brain-Inspired Mechanisms in Continual Learning
Humans excel at continually acquiring, consolidating, and retaining information from an ever-changing environment, whereas artificial neural networks (ANNs) exhibit catastrophic forgetting.
Error Sensitivity Modulation based Experience Replay: Mitigating Abrupt Representation Drift in Continual Learning
To this end, we propose \textit{ESMER} which employs a principled mechanism to modulate error sensitivity in a dual-memory rehearsal-based system.
Sparse Coding in a Dual Memory System for Lifelong Learning
Efficient continual learning in humans is enabled by a rich set of neurophysiological mechanisms and interactions between multiple memory systems.
SYNERgy between SYNaptic consolidation and Experience Replay for general continual learning
Continual learning (CL) in the brain is facilitated by a complex set of mechanisms.
Learning Fast, Learning Slow: A General Continual Learning Method based on Complementary Learning System
Humans excel at continually learning from an ever-changing environment whereas it remains a challenge for deep neural networks which exhibit catastrophic forgetting.
Noisy Concurrent Training for Efficient Learning under Label Noise
Thus, we propose Noisy Concurrent Training (NCT) which leverages collaborative learning to use the consensus between two models as an additional source of supervision.
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Adversarial Concurrent Training: Optimizing Robustness and Accuracy Trade-off of Deep Neural Networks
Adversarial training has been proven to be an effective technique for improving the adversarial robustness of models.
Knowledge Distillation Beyond Model Compression
Knowledge distillation (KD) is commonly deemed as an effective model compression technique in which a compact model (student) is trained under the supervision of a larger pretrained model or an ensemble of models (teacher).
Noise as a Resource for Learning in Knowledge Distillation
In doing so, we propose three different methods that target the common challenges in deep neural networks: minimizing the performance gap between a compact model and large model (Fickle Teacher), training high performance compact adversarially robust models (Soft Randomization), and training models efficiently under label noise (Messy Collaboration).
