Search Results for author:
Found 5 papers, 3 papers with code
When Foresight Pruning Meets Zeroth-Order Optimization: Efficient Federated Learning for Low-Memory Devices
Comprehensive experimental results obtained from simulation- and real test-bed-based platforms show that our federated foresight-pruning method not only preserves the ability of the dense model with a memory reduction up to 9x but also boosts the performance of the vanilla BP-Free method with dramatically fewer FLOPs.
Situation-Dependent Causal Influence-Based Cooperative Multi-agent Reinforcement Learning
To this end, in this paper, we propose a novel MARL algorithm named Situation-Dependent Causal Influence-Based Cooperative Multi-agent Reinforcement Learning (SCIC), which incorporates a novel Intrinsic reward mechanism based on a new cooperation criterion measured by situation-dependent causal influence among agents.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
FAIR: Frequency-aware Image Restoration for Industrial Visual Anomaly Detection
Image reconstruction-based anomaly detection models are widely explored in industrial visual inspection.
Ranked #14 on
Anomaly Detection
on VisA
Component-aware anomaly detection framework for adjustable and logical industrial visual inspection
Meanwhile, segmenting a product image into multiple components provides a novel perspective for industrial visual inspection, demonstrating great potential in model customization, noise resistance, and anomaly classification.
Ranked #8 on
Anomaly Detection
on MVTec LOCO AD
Reconstruction from edge image combined with color and gradient difference for industrial surface anomaly detection
The model with an overly strong generalization capability can even well reconstruct the abnormal regions, making them less distinguishable, while the model with a poor generalization capability can not reconstruct those changeable high-frequency components in the normal regions, which ultimately leads to false positives.
Ranked #15 on
Anomaly Detection
on VisA
