8 citations · 17 across the 6 of their papers we have counts for
5 papers · 1 filter
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…
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…