Convolution
Description
A convolution is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.
Intuitively, a convolution allows for weight sharing - reducing the number of effective parameters - and image translation (allowing for the same feature to be detected in different parts of the input space).
Image Source: https://arxiv.org/pdf/1603.07285.pdf
Papers Using This Method
Joint angle model based learning to refine kinematic human pose estimation2025-07-15Towards Imperceptible JPEG Image Hiding: Multi-range Representations-driven Adversarial Stego Generation2025-07-11Admissibility of Stein Shrinkage for Batch Normalization in the Presence of Adversarial Attacks2025-07-11HVI-CIDNet+: Beyond Extreme Darkness for Low-Light Image Enhancement2025-07-09Normalizing Diffusion Kernels with Optimal Transport2025-07-08Aliasing in Convnets: A Frame-Theoretic Perspective2025-07-08ECORE: Energy-Conscious Optimized Routing for Deep Learning Models at the Edge2025-07-08Detecting and Mitigating Reward Hacking in Reinforcement Learning Systems: A Comprehensive Empirical Study2025-07-082048: Reinforcement Learning in a Delayed Reward Environment2025-07-07Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining2025-07-07MVNet: Hyperspectral Remote Sensing Image Classification Based on Hybrid Mamba-Transformer Vision Backbone Architecture2025-07-06RLHGNN: Reinforcement Learning-driven Heterogeneous Graph Neural Network for Next Activity Prediction in Business Processes2025-07-03Determination Of Structural Cracks Using Deep Learning Frameworks2025-07-03YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection2025-06-26StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning2025-06-26Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios2025-06-25FINN-GL: Generalized Mixed-Precision Extensions for FPGA-Accelerated LSTMs2025-06-25U-R-VEDA: Integrating UNET, Residual Links, Edge and Dual Attention, and Vision Transformer for Accurate Semantic Segmentation of CMRs2025-06-25Multi-Objective Reinforcement Learning for Cognitive Radar Resource Management2025-06-25ReCoGNet: Recurrent Context-Guided Network for 3D MRI Prostate Segmentation2025-06-24