In the second stage, the deep visual, shallow visual, and text features are extracted for fusion to identify the category blocks of documents.
(2) A novel Feature Matching Loss that allows knowledge distillation from large denoising networks in the form of a perceptual content loss.
We introduce tf_geometric, an efficient and friendly library for graph deep learning, which is compatible with both TensorFlow 1. x and 2. x.
We experimentally demonstrated that the AYFFF self-assemblies adsorbed with various monovalent cations (Na+, K+, and Li+) show unexpectedly super strong paramagnetism.
The detection and characterization of partial discharge (PD) are crucial for the insulation diagnosis of overhead lines with covered conductors.
Pre-trained models such as BERT are widely used in NLP tasks and are fine-tuned to improve the performance of various NLP tasks consistently.
Non-intrusive load monitoring addresses the challenging task of decomposing the aggregate signal of a household's electricity consumption into appliance-level data without installing dedicated meters.
This paper develops a novel graph convolutional network (GCN) framework for fault location in power distribution networks.
A convolutional sequence to sequence non-intrusive load monitoring model is proposed in this paper.
We present a novel method for aligning images in an HDR (high-dynamic-range) image stack to produce a new exposure stack where all the images are aligned and appear as if they were taken simultaneously, even in the case of highly dynamic scenes.