11 citations · 34 across the 20 of their papers we have counts for
5 papers · 1 filter
Are Sparse Neural Networks Better Hard Sample Learners?
Qiao Xiao, Boqian Wu, Lu Yin +4
While deep learning has demonstrated impressive progress, it remains a daunting challenge to learn from hard samples as these samples are usually noisy and intricate. These hard sa…
(PASS) Visual Prompt Locates Good Structure Sparsity through a Recurrent HyperNetwork
Tianjin Huang, Fang Meng, Li Shen +5
Large-scale neural networks have demonstrated remarkable performance in different domains like vision and language processing, although at the cost of massive computation resources…
The Counterattack of CNNs in Self-Supervised Learning: Larger Kernel Size might be All You Need
Tianjin Huang, Tianlong Chen, Zhangyang Wang +1
Vision Transformers have been rapidly uprising in computer vision thanks to their outstanding scaling trends, and gradually replacing convolutional neural networks (CNNs). Recent w…
Visual Prompting Upgrades Neural Network Sparsification: A Data-Model Perspective
Can Jin, Tianjin Huang, Yihua Zhang +4
The rapid development of large-scale deep learning models questions the affordability of hardware platforms, which necessitates the pruning to reduce their computational and memory…
Are Large Kernels Better Teachers than Transformers for ConvNets?
Tianjin Huang, Lu Yin, Zhenyu Zhang +5
This paper reveals a new appeal of the recently emerged large-kernel Convolutional Neural Networks (ConvNets): as the teacher in Knowledge Distillation (KD) for small-kernel ConvNe…