37 citations · 106 across the 17 of their papers we have counts for
5 papers · 1 filter
Understanding and Combating Robust Overfitting via Input Loss Landscape Analysis and Regularization
Lin Li, Michael Spratling
Adversarial training is widely used to improve the robustness of deep neural networks to adversarial attack. However, adversarial training is prone to overfitting, and the cause is…
CobNet: Cross Attention on Object and Background for Few-Shot Segmentation
Haoyan Guan, Michael Spratling
Few-shot segmentation aims to segment images containing objects from previously unseen classes using only a few annotated samples. Most current methods focus on using object inform…
On the biological plausibility of orthogonal initialisation for solving gradient instability in deep neural networks
Nikolay Manchev, Michael Spratling
Initialising the synaptic weights of artificial neural networks (ANNs) with orthogonal matrices is known to alleviate vanishing and exploding gradient problems. A major objection a…
Query Semantic Reconstruction for Background in Few-Shot Segmentation
Haoyan Guan, Michael Spratling
Few-shot segmentation (FSS) aims to segment unseen classes using a few annotated samples. Typically, a prototype representing the foreground class is extracted from annotated suppo…
Registration based Few-Shot Anomaly Detection
Chaoqin Huang, Haoyan Guan, Aofan Jiang +3
This paper considers few-shot anomaly detection (FSAD), a practical yet under-studied setting for anomaly detection (AD), where only a limited number of normal images are provided…