most citedOn Generalizing Beyond Domains in Cross-Domain Continual Learning

3 citations · 3 across the 3 of their papers we have counts for

collaborators

5 papers

cs.LG20223 cited

On Generalizing Beyond Domains in Cross-Domain Continual Learning

Christian Simon, Masoud Faraki, Yi-Hsuan Tsai +5

Humans have the ability to accumulate knowledge of new tasks in varying conditions, but deep neural networks often suffer from catastrophic forgetting of previously learned knowled…

cs.CV2021

Meta-Learning for Multi-Label Few-Shot Classification

Christian Simon, Piotr Koniusz, Mehrtash Harandi

Even with the luxury of having abundant data, multi-label classification is widely known to be a challenging task to address. This work targets the problem of multi-label meta-lear…

cs.LG2021

Towards a Robust Differentiable Architecture Search under Label Noise

Christian Simon, Piotr Koniusz, Lars Petersson +2

Neural Architecture Search (NAS) is the game changer in designing robust neural architectures. Architectures designed by NAS outperform or compete with the best manual network desi…

cs.LG2021

On Learning the Geodesic Path for Incremental Learning

Christian Simon, Piotr Koniusz, Mehrtash Harandi

Neural networks notoriously suffer from the problem of catastrophic forgetting, the phenomenon of forgetting the past knowledge when acquiring new knowledge. Overcoming catastrophi…

cs.CV2021

Reinforced Attention for Few-Shot Learning and Beyond

Jie Hong, Pengfei Fang, Weihao Li +4

Few-shot learning aims to correctly recognize query samples from unseen classes given a limited number of support samples, often by relying on global embeddings of images. In this…