no code implementations • 29 Sep 2021 • Honglin Li, Frieder Ganz, David J. Sharp, Payam M. Barnaghi
The proposed model can continually learn and embed new tasks into the model without losing the information about previously learned tasks.
1 code implementation • CVPR 2021 • Ji Lin, Richard Zhang, Frieder Ganz, Song Han, Jun-Yan Zhu
Generative adversarial networks (GANs) have enabled photorealistic image synthesis and editing.
no code implementations • 19 Oct 2020 • Honglin Li, Yifei Fan, Frieder Ganz, Anthony Yezzi, Payam Barnaghi
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.
no code implementations • 8 May 2020 • Honglin Li, Payam Barnaghi, Shirin Enshaeifar, Frieder Ganz
The changes in goals or data are referred to as new tasks in a continual learning model.
no code implementations • 9 Oct 2019 • Honglin Li, Payam Barnaghi, Shirin Enshaeifar, Frieder Ganz
The catastrophic forgetting is an inevitable problem in continual learning models for dynamic environments.
no code implementations • 20 May 2019 • Honglin Li, Shirin Enshaeifar, Frieder Ganz, Payam Barnaghi
The results show that our approach enables the model to continually learn and adapt to the new changes without forgetting the previously learned tasks.
no code implementations • 6 Nov 2018 • Honglin Li, Frieder Ganz, Shirin Enshaeifar, Payam Barnaghi
Learning in a non-stationary environment is an inevitable problem when applying machine learning algorithm to real world environment.