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This repository contains convolutional neural network (CNN) models trained on ImageNet by Marcel Simon at the Computer Vision Group Jena (CVGJ) using the Caffe framework as published in the accompanying technical report. Each model is in a separate subfolder and contains everything needed to reproduce the results. This repository focuses currently contains the batch-normalization-variants of AlexNet and VGG19 as well as the training code for Residual Networks (Resnet). No mean subtraction is required for the pre-trained models! We have a batch-normalization layer which basically does the same.
In the Jupyter notebook for this repository, I begin by calculating the bottleneck features for the CIFAR-10 dataset. These features are then visualized with a Barnes-Hut implementation of t-SNE, which is the fastest t-SNE implementation to date.
The original Matlab implementation and paper (for AlexNet, GoogLeNet, and VGG16) can be found here. A Keras implementation of VGG-CAM can be found here. This implementation is written in Keras and uses ResNet-50, which was not explored in the original paper.
This repository contains implementations of various deep learning research papers. The models are broadly categorised into the folders Generative (e.g. various generative models), NLP (e.g. various recurrent neural networks (RNNs) and natural language processing (NLP) models), Classification (e.g. various CNN models to classify images), Object Detection, Multimodal , Super resolution , 3D Computer Vision. See the READMEs of respective models for more information.