8 citations · 17 across the 6 of their papers we have counts for
6 papers
AGAD: Adversarial Generative Anomaly Detection
Jian Shi, Ni Zhang
Anomaly detection suffered from the lack of anomalies due to the diversity of abnormalities and the difficulties of obtaining large-scale anomaly data. Semi-supervised anomaly dete…
Dissolving Is Amplifying: Towards Fine-Grained Anomaly Detection
Jian Shi, Pengyi Zhang, Ni Zhang +2
Medical imaging often contains critical fine-grained features, such as tumors or hemorrhages, crucial for diagnosis yet potentially too subtle for detection with conventional metho…
TorMentor: Deterministic dynamic-path, data augmentations with fractals
Anguelos Nicolaou, Vincent Christlein, Edgar Riba +3
We propose the use of fractals as a means of efficient data augmentation. Specifically, we employ plasma fractals for adapting global image augmentation transformations into contin…
Upsampling Autoencoder for Self-Supervised Point Cloud Learning
Cheng Zhang, Jian Shi, Xuan Deng +1
In computer-aided design (CAD) community, the point cloud data is pervasively applied in reverse engineering, where the point cloud analysis plays an important role. While a large…
DeepCatra: Learning Flow- and Graph-based Behaviors for Android Malware Detection
Yafei Wu, Jian Shi, Peicheng Wang +2
As Android malware is growing and evolving, deep learning has been introduced into malware detection, resulting in great effectiveness. Recent work is considering hybrid models and…
Differentiable Data Augmentation with Kornia
Jian Shi, Edgar Riba, Dmytro Mishkin +2
In this paper we present a review of the Kornia differentiable data augmentation (DDA) module for both for spatial (2D) and volumetric (3D) tensors. This module leverages different…