62 citations · 95 across the 5 of their papers we have counts for
6 papers
Density Map Distillation for Incremental Object Counting
Chenshen Wu, Joost van de Weijer
We investigate the problem of incremental learning for object counting, where a method must learn to count a variety of object classes from a sequence of datasets. A naïve approach…
Positive Pair Distillation Considered Harmful: Continual Meta Metric Learning for Lifelong Object Re-Identification
Kai Wang, Chenshen Wu, Andy Bagdanov +4
Lifelong object re-identification incrementally learns from a stream of re-identification tasks. The objective is to learn a representation that can be applied to all tasks and tha…
MineGAN++: Mining Generative Models for Efficient Knowledge Transfer to Limited Data Domains
Yaxing Wang, Abel Gonzalez-Garcia, Chenshen Wu +4
GANs largely increases the potential impact of generative models. Therefore, we propose a novel knowledge transfer method for generative models based on mining the knowledge that i…
Generative Feature Replay For Class-Incremental Learning
Xialei Liu, Chenshen Wu, Mikel Menta +5
Humans are capable of learning new tasks without forgetting previous ones, while neural networks fail due to catastrophic forgetting between new and previously-learned tasks. We co…
Memory Replay GANs: learning to generate images from new categories without forgetting
Chenshen Wu, Luis Herranz, Xialei Liu +3
Previous works on sequential learning address the problem of forgetting in discriminative models. In this paper we consider the case of generative models. In particular, we investi…
Transferring GANs: generating images from limited data
Yaxing Wang, Chenshen Wu, Luis Herranz +3
Transferring the knowledge of pretrained networks to new domains by means of finetuning is a widely used practice for applications based on discriminative models. To the best of ou…