The existing contrastive learning methods widely adopt one-hot instance
...
Asymmetric appearance between positive pair effectively reduces the risk...
Federated learning achieves joint training of deep models by connecting
...
The dynamics of temporal networks lie in the continuous interactions bet...
Knowledge distillation (KD) aims to craft a compact student model that
i...
Model inversion, whose goal is to recover training data from a pre-train...
Generative Adversarial Networks (GANs) have demonstrated unprecedented
s...
Knowledge distillation has demonstrated encouraging performances in deep...
Exploring the intrinsic interconnections between the knowledge encoded i...
Knowledge Distillation (KD) has made remarkable progress in the last few...
Exploring the transferability between heterogeneous tasks sheds light on...
A massive number of well-trained deep networks have been released by
dev...
With the rapid development of deep learning, there have been an
unpreced...
In this paper, we introduce a selective zero-shot classification problem...
Most existing Zero-Shot Learning (ZSL) methods have the strong bias prob...