Search Results for author:
Found 10 papers, 4 papers with code
SVDFormer: Complementing Point Cloud via Self-view Augmentation and Self-structure Dual-generator
In this paper, we propose a novel network, SVDFormer, to tackle two specific challenges in point cloud completion: understanding faithful global shapes from incomplete point clouds and generating high-accuracy local structures.
FractalAD: A simple industrial anomaly detection method using fractal anomaly generation and backbone knowledge distillation
Moreover, we designed a backbone knowledge distillation structure to extract prior knowledge contained in normal samples.
Xiaoicesing 2: A High-Fidelity Singing Voice Synthesizer Based on Generative Adversarial Network
XiaoiceSing is a singing voice synthesis (SVS) system that aims at generating 48kHz singing voices.
HiFi-WaveGAN: Generative Adversarial Network with Auxiliary Spectrogram-Phase Loss for High-Fidelity Singing Voice Generation
Entertainment-oriented singing voice synthesis (SVS) requires a vocoder to generate high-fidelity (e. g. 48kHz) audio.
Discriminative-Generative Representation Learning for One-Class Anomaly Detection
As a kind of generative self-supervised learning methods, generative adversarial nets have been widely studied in the field of anomaly detection.
Ghost Handwritten Digit Recognition based on Deep Learning
We present a ghost handwritten digit recognition method for the unknown handwritten digits based on ghost imaging (GI) with deep neural network, where a few detection signals from the bucket detector, generated by the Cosine Transform speckle, are used as the characteristic information and the input of the designed deep neural network (DNN), and the classification is designed as the output of the DNN.
Adversarial Feature Learning of Online Monitoring Data for Operational Risk Assessment in Distribution Networks
(2) operational risk assessment.
Spectrum concentration in deep residual learning: a free probability approach
We revisit the initialization of deep residual networks (ResNets) by introducing a novel analytical tool in free probability to the community of deep learning.
An Accurate and Real-time Self-blast Glass Insulator Location Method Based On Faster R-CNN and U-net with Aerial Images
The location of broken insulators in aerial images is a challenging task.
A Random Matrix Theoretical Approach to Early Event Detection in Smart Grid
With the statistical procedure, the proposed method is universal and fast; moreover, it is robust against traditional EED challenges (such as error accumulations, spurious correlations, and even bad data in core area).
