LeNet

Computer VisionIntroduced 199845 papers

Description

LeNet is a classic convolutional neural network employing the use of convolutions, pooling and fully connected layers. It was used for the handwritten digit recognition task with the MNIST dataset. The architectural design served as inspiration for future networks such as AlexNet and VGG..

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Papers Using This Method

Improving Classification Neural Networks by using Absolute activation function (MNIST/LeNET-5 example)2023-04-23Convolutional neural networks for Alzheimer’s disease detection on MRI images2021-04-29Hyperparameter Ensembles for Robustness and Uncertainty Quantification2020-06-24MNIST-MIX: A Multi-language Handwritten Digit Recognition Dataset2020-04-08LCP: A Low-Communication Parallelization Method for Fast Neural Network Inference in Image Recognition2020-03-13Can we have it all? On the Trade-off between Spatial and Adversarial Robustness of Neural Networks2020-02-26Identifying Critical Neurons in ANN Architectures using Mixed Integer Programming2020-02-17Non-linear Neurons with Human-like Apical Dendrite Activations2020-02-02Analytical Moment Regularizer for Training Robust Networks2020-01-01Learn-able parameter guided Activation Functions2019-12-23AutoPrune: Automatic Network Pruning by Regularizing Auxiliary Parameters2019-12-01FeCaffe: FPGA-enabled Caffe with OpenCL for Deep Learning Training and Inference on Intel Stratix 102019-11-18Coverage Testing of Deep Learning Models using Dataset Characterization2019-11-172L-3W: 2-Level 3-Way Hardware-Software Co-Verification for the Mapping of Deep Learning Architecture (DLA) onto FPGA Boards2019-11-14Variational Student: Learning Compact and Sparser Networks in Knowledge Distillation Framework2019-10-26Offline handwritten mathematical symbol recognition utilising deep learning2019-10-16SPEC2: SPECtral SParsE CNN Accelerator on FPGAs2019-10-16Soft-Label Dataset Distillation and Text Dataset Distillation2019-10-06Defending Against Adversarial Attacks by Suppressing the Largest Eigenvalue of Fisher Information Matrix2019-09-13Adversarial Robustness via Label-Smoothing2019-06-27