Weight-sharing neural architecture search: A battle to shrink the optimization gap
Neural architecture search (NAS) has attracted increasing attention. In recent years,
individual search methods have been replaced by weight-sharing search methods for higher …
individual search methods have been replaced by weight-sharing search methods for higher …
Deep high-resolution representation learning for visual recognition
High-resolution representations are essential for position-sensitive vision problems, such as
human pose estimation, semantic segmentation, and object detection. Existing state-of-the …
human pose estimation, semantic segmentation, and object detection. Existing state-of-the …
Deep high-resolution representation learning for human pose estimation
In this paper, we are interested in the human pose estimation problem with a focus on
learning reliable high-resolution representations. Most existing methods recover high …
learning reliable high-resolution representations. Most existing methods recover high …
Short-term load forecasting with deep residual networks
We present in this paper a model for forecasting short-term electric load based on deep
residual networks. The proposed model is able to integrate domain knowledge and …
residual networks. The proposed model is able to integrate domain knowledge and …
COVID-19 chest CT image segmentation--a deep convolutional neural network solution
A novel coronavirus disease 2019 (COVID-19) was detected and has spread rapidly across
various countries around the world since the end of the year 2019, Computed Tomography …
various countries around the world since the end of the year 2019, Computed Tomography …
COVID-19 chest CT image segmentation network by multi-scale fusion and enhancement operations
A novel coronavirus disease 2019 (COVID-19) was detected and has spread rapidly across
various countries around the world since the end of the year 2019. Computed Tomography …
various countries around the world since the end of the year 2019. Computed Tomography …
Channel splitting network for single MR image super-resolution
High resolution magnetic resonance (MR) imaging is desirable in many clinical applications
due to its contribution to more accurate subsequent analyses and early clinical diagnoses …
due to its contribution to more accurate subsequent analyses and early clinical diagnoses …
Interleaved structured sparse convolutional neural networks
In this paper, we study the problem of designing efficient convolutional neural network
architectures with the interest in eliminating the redundancy in convolution kernels. In …
architectures with the interest in eliminating the redundancy in convolution kernels. In …
Igcv3: Interleaved low-rank group convolutions for efficient deep neural networks
In this paper, we are interested in building lightweight and efficient convolutional neural
networks. Inspired by the success of two design patterns, composition of structured sparse …
networks. Inspired by the success of two design patterns, composition of structured sparse …
[PDF][PDF] Demystifying local vision transformer: Sparse connectivity, weight sharing, and dynamic weight
Abstract Vision Transformer (ViT) attains state-of-the-art performance in visual recognition,
and the variant, Local Vision Transformer, makes further improvements. The major …
and the variant, Local Vision Transformer, makes further improvements. The major …