no code implementations • ICLR 2019 • Shixian Wen, Laurent Itti
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).
1 code implementation • 24 May 2023 • Yunhao Ge, Yuecheng Li, Di wu, Ao Xu, Adam M. Jones, Amanda Sofie Rios, Iordanis Fostiropoulos, Shixian Wen, Po-Hsuan Huang, Zachary William Murdock, Gozde Sahin, Shuo Ni, Kiran Lekkala, Sumedh Anand Sontakke, Laurent Itti
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.
no code implementations • 20 Jan 2022 • Shixian Wen, Amanda Sofie Rios, Kiran Lekkala, Laurent Itti
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.
no code implementations • 27 Sep 2020 • Shixian Wen, Amanda Rios, Laurent Itti
The reason is that neural networks fail to accommodate the distribution drift of the input data caused by adversarial perturbations.
no code implementations • 27 Sep 2020 • Shixian Wen, Amanda Rios, Yunhao Ge, Laurent Itti
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.
no code implementations • 9 Oct 2019 • Shixian Wen, Laurent Itti
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.
no code implementations • 22 Jun 2019 • Shixian Wen, Laurent Itti
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.
no code implementations • 18 May 2018 • Shixian Wen, Laurent Itti
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).