Squeeze-and-Excitation Networks

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Convolutional neural networks are built upon the convolution operation, which extracts informative features by fusing spatial and channel-wise information together within local receptive fields. In order to boost the representational power of a network, much existing work has shown the benefits of enhancing spatial encoding. In this work, we focus on channels and propose a novel architectural unit, which we term the "Squeeze-and-Excitation" (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels. We demonstrate that by stacking these blocks together, we can construct SENet architectures that generalise extremely well across challenging datasets. Crucially, we find that SE blocks produce significant performance improvements for existing state-of-the-art deep architectures at slight computational cost. SENets formed the foundation of our ILSVRC 2017 classification submission which won first place and significantly reduced the top-5 error to 2.251%, achieving a 25% relative improvement over the winning entry of 2016.

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Video embeddings for retrieval - code for the paper "Use What You Have: Video retrieval using representations from collaborative experts"

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Squeeze-and-Excitation Networks

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Memory consumption and FLOP count estimates for convnets

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Implementation of SENets by chainer (Squeeze-and-Excitation Networks: https://arxiv.org/abs/1709.01507)

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:fire::fire: A MXNet implementation of Squeeze-and-Excitation Networks (SE-ResNext, SE-Resnet, SE-Inception-v4 and SE-Inception-Resnet-v2) :fire::fire:

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Squeeze-and-Excitation module for convolutional neural networks written in PyTorch

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SE-Net Incorporates with ResNet and WideResnet on CIFAR-10/100 Dataset.

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Naive implementation of SENet

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List of books that I like related to deep learning

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github.com: /ahtwq/SENet

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PyTorch implementation of SENet

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The solution for the Carvana Image Masking Challenge on Kaggle. It uses a custom version of RefineNet with Squeeze-and-Excitation modules implemented in PyTorch. It was a part of the final ensemble that was ranked 23 out of 735 teams (top 4%).

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Implementation of IndRNN in Tensorflow

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Repository for Deep Learning course (Fall 2018 - CS-GY 9223-H). Building MLP, Conv Net, LSTM, GAN, DCGAN models using low-level and high-level APIs

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PyTorch implementation of SENet

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Implementation of Squeeze-and-Excitiation Network on Keras

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implementation of squeze excitation moduels in keras

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Squeeze-and-Excitation Networks

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Experimental keras implementation of novel neural network structures

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An convolutional neural network that detects metastatic cancer in small image patches taken from larger digital pathology scans

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Using cifar-10 datasets to learn deep learning.

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Collection of Keras models used for classification

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Implementation of Squeeze and Excitation Networks in Keras

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A squeeze-and-excitation enabled ResNet for image classification

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Deep Learning Architecture Genealogy Project

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