515 citations · 1.3k across the 26 of their papers we have counts for
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CleftNet: Augmented Deep Learning for Synaptic Cleft Detection from Brain Electron Microscopy
Yi Liu, Shuiwang Ji
Detecting synaptic clefts is a crucial step to investigate the biological function of synapses. The volume electron microscopy (EM) allows the identification of synaptic clefts by…
Towards Improved and Interpretable Deep Metric Learning via Attentive Grouping
Xinyi Xu, Zhengyang Wang, Cheng Deng +2
Grouping has been commonly used in deep metric learning for computing diverse features. However, current methods are prone to overfitting and lack interpretability. In this work, w…
Noise2Same: Optimizing A Self-Supervised Bound for Image Denoising
Yaochen Xie, Zhengyang Wang, Shuiwang Ji
Self-supervised frameworks that learn denoising models with merely individual noisy images have shown strong capability and promising performance in various image denoising tasks.…
Kronecker Attention Networks
Hongyang Gao, Zhengyang Wang, Shuiwang Ji
Attention operators have been applied on both 1-D data like texts and higher-order data such as images and videos. Use of attention operators on high-order data requires flattening…
ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions
Hongyang Gao, Zhengyang Wang, Shuiwang Ji
Convolutional neural networks (CNNs) have shown great capability of solving various artificial intelligence tasks. However, the increasing model size has raised challenges in emplo…
Dense Transformer Networks
Jun Li, Yongjun Chen, Lei Cai +2
The key idea of current deep learning methods for dense prediction is to apply a model on a regular patch centered on each pixel to make pixel-wise predictions. These methods are l…