Tensorflow Implementation of Visualization Regularizers for Neural Network based Image Recognition
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Updated
Dec 26, 2016 - Python
Tensorflow Implementation of Visualization Regularizers for Neural Network based Image Recognition
The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.
Comprehensive image classification for training multilayer perceptron (MLP), LeNet, LeNet5, conv2, conv4, conv6, VGG11, VGG13, VGG16, VGG19 with batch normalization, ResNet18, ResNet34, ResNet50, MobilNetV2 on MNIST, CIFAR10, CIFAR100, and ImageNet1K.
Classification using a Multi-Layer Perceptron neural network from scratch
Implementations of different loss-correction techniques to help deep models learn under class-conditional label noise.
Implementation of semi and self supervised learning on Imbalanced Dataset
A set of scripts and experiments making it easier to analyze deep learning empirically.
Pruning conv neural network by decreasing of num_filters in Resnet20
CIFAR-10 Dataset Image classification using Convolutional Neural Networks with Keras
Detect and Analyze Trojan attacks on deep neural networks that are designed to be difficult to detect.
One-offs.
Source code for HTD (WACV 2019)
CIFAR-10 Image Classification using PyTorch
Implementation of optimization and regularization algorithms in deep neural networks from scratch
Improved CNN Training and Visualization
ResNet for CIFAR with Estimator API and tf.keras.Model class
An easy template for Cifar classification using Pytorch
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