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Found 6 papers, 4 papers with code
Representational Continuity for Unsupervised Continual Learning
Continual learning (CL) aims to learn a sequence of tasks without forgetting the previously acquired knowledge.
Online Coreset Selection for Rehearsal-based Continual Learning
We validate the effectiveness of our coreset selection mechanism over various standard, imbalanced, and noisy datasets against strong continual learning baselines, demonstrating that it improves task adaptation and prevents catastrophic forgetting in a sample-efficient manner.
Learning to Generate Noise for Multi-Attack Robustness
Adversarial learning has emerged as one of the successful techniques to circumvent the susceptibility of existing methods against adversarial perturbations.
Adversarial Neural Pruning with Latent Vulnerability Suppression
Despite the remarkable performance of deep neural networks on various computer vision tasks, they are known to be susceptible to adversarial perturbations, which makes it challenging to deploy them in real-world safety-critical applications.
Learning Sparse Networks Using Targeted Dropout
Before computing the gradients for each weight update, targeted dropout stochastically selects a set of units or weights to be dropped using a simple self-reinforcing sparsity criterion and then computes the gradients for the remaining weights.
VayuAnukulani: Adaptive Memory Networks for Air Pollution Forecasting
Extensive experiments on data sources obtained in Delhi demonstrate that the proposed adaptive attention based Bidirectional LSTM Network outperforms several baselines for classification and regression models.
