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Found 7 papers, 1 papers with code
Continual Learning Using Task Conditional Neural Networks
The proposed model can continually learn and embed new tasks into the model without losing the information about previously learned tasks.
Anycost GANs for Interactive Image Synthesis and Editing
Generative adversarial networks (GANs) have enabled photorealistic image synthesis and editing.
Verifying the Causes of Adversarial Examples
The robustness of neural networks is challenged by adversarial examples that contain almost imperceptible perturbations to inputs, which mislead a classifier to incorrect outputs in high confidence.
Continual Learning Using Multi-view Task Conditional Neural Networks
The changes in goals or data are referred to as new tasks in a continual learning model.
Continual Learning Using Bayesian Neural Networks
The catastrophic forgetting is an inevitable problem in continual learning models for dynamic environments.
Continual Learning in Deep Neural Network by Using a Kalman Optimiser
The results show that our approach enables the model to continually learn and adapt to the new changes without forgetting the previously learned tasks.
Kalman Filter Modifier for Neural Networks in Non-stationary Environments
Learning in a non-stationary environment is an inevitable problem when applying machine learning algorithm to real world environment.
