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Methods/ReLU6

ReLU6

GeneralIntroduced 200095 papers
Source Paper

Description

ReLU6 is a modification of the rectified linear unit where we limit the activation to a maximum size of 666. This is due to increased robustness when used with low-precision computation.

Image Credit: PyTorch

Papers Using This Method

Comparative Analysis of Lightweight Deep Learning Models for Memory-Constrained Devices2025-05-06VeLU: Variance-enhanced Learning Unit for Deep Neural Networks2025-04-21Time Frequency Analysis of EMG Signal for Gesture Recognition using Fine grained Features2025-04-20A Light Perspective for 3D Object Detection2025-03-10Pulmonary Tuberculosis Edge Diagnosis System Based on MindSpore Framework: Low-cost and High-precision Implementation with Ascend 310 Chip2025-02-15HyperCam: Low-Power Onboard Computer Vision for IoT Cameras2025-01-17Revolutionizing Communication with Deep Learning and XAI for Enhanced Arabic Sign Language Recognition2025-01-14Scalable Forward-Forward Algorithm2025-01-06Towards Efficient IMC Accelerator Design Through Joint Hardware-Workload Co-optimization2024-10-22Efficient Deep Learning Board: Training Feedback Is Not All You Need2024-10-17Real-time Monitoring of Lower Limb Movement Resistance Based on Deep Learning2024-10-13POMONAG: Pareto-Optimal Many-Objective Neural Architecture Generator2024-09-30RNC: Efficient RRAM-aware NAS and Compilation for DNNs on Resource-Constrained Edge Devices2024-09-27PlainUSR: Chasing Faster ConvNet for Efficient Super-Resolution2024-09-20WaterMAS: Sharpness-Aware Maximization for Neural Network Watermarking2024-09-05Analysis of Modern Computer Vision Models for Blood Cell Classification2024-06-30HASS: Hardware-Aware Sparsity Search for Dataflow DNN Accelerator2024-06-05Vision-Based Approach for Food Weight Estimation from 2D Images2024-05-26Impact of Synthetic Images on Morphing Attack Detection Using a Siamese Network2024-03-14A New Machine Learning Dataset of Bulldog Nostril Images for Stenosis Degree Classification2024-03-11