Search Results for author:
Found 8 papers, 1 papers with code
Overcoming catastrophic forgetting through weight consolidation and long-term memory
We obtain similar results with a much more difficult disjoint CIFAR10 task (70. 10% initial task 1 performance, 67. 73% after learning tasks 2 and 3 for AD+EWC, while PGD and EWC both fall to chance level).
Lightweight Learner for Shared Knowledge Lifelong Learning
We propose a new Shared Knowledge Lifelong Learning (SKILL) challenge, which deploys a decentralized population of LL agents that each sequentially learn different tasks, with all agents operating independently and in parallel.
What can we learn from misclassified ImageNet images?
Hence, we propose a two-stage Super-Sub framework, and demonstrate that: (i) The framework improves overall classification performance by 3. 3%, by first inferring a superclass using a generalist superclass-level network, and then using a specialized network for final subclass-level classification.
Beneficial Perturbations Network for Defending Adversarial Examples
The reason is that neural networks fail to accommodate the distribution drift of the input data caused by adversarial perturbations.
Beneficial Perturbation Network for designing general adaptive artificial intelligence systems
Continual learning of multiple tasks in artificial neural networks using gradient descent leads to catastrophic forgetting, whereby a previously learned mapping of an old task is erased when learning new mappings for new tasks.
Adversarial Training: embedding adversarial perturbations into the parameter space of a neural network to build a robust system
Adversarial training, in which a network is trained on both adversarial and clean examples, is one of the most trusted defense methods against adversarial attacks.
Beneficial perturbation network for continual learning
Sequential learning of multiple tasks in artificial neural networks using gradient descent leads to catastrophic forgetting, whereby previously learned knowledge is erased during learning of new, disjoint knowledge.
Overcoming catastrophic forgetting problem by weight consolidation and long-term memory
We apply our method to sequentially learning to classify digits 0, 1, 2 (task 1), 4, 5, 6, (task 2), and 7, 8, 9 (task 3) in MNIST (disjoint MNIST task).
