1. DER: dynamically expandable representation for class in-crementallearning;yan;Proceedings of the IEEE Con-ference on Computer Vision and Pattern Recognition (CVPR),0
2. Failure modes of variational autoencoders and their effects on downstream tasks;yacoby;ICML 2020 Work-shop on Uncertainty and Robustness in Deep Learning,2021
3. The Caltech-UCSD Birds-200–2011 Dataset;wah;Technical Report CNS- TR-2011-001 California Institute of Technol-ogy,2011
4. Rehearsal revealed: The limits and merits of revisiting samples in continual learning
5. A Global Geometric Framework for Nonlinear Dimensionality Reduction