An Effective Training Method For Deep Convolutional Neural Network
In this paper, we propose the nonlinearity generation method to speed up and stabilize the training of deep convolutional neural networks. The proposed method modifies a family of activation functions as nonlinearity generators (NGs). NGs make the activation functions linear symmetric for their inputs to lower model capacity, and automatically introduce nonlinearity to enhance the capacity of the
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (e.g., 10-150 MFLOPs). The new architecture utilizes two new operations, pointwise group convolution and channel shuffle, to greatly reduce computation cost while maintaining accuracy. Experiments on ImageNet classification and MS COCO
Simple Convolutional Neural Network for Genomic Variant Calling with TensorFlow
AI/Machine Learning in Biotech StartupsNo doubt that the fast development of recent progress using deep neural network has change the way that we can solve various problem from image recognition to genomics. Many startups (Viome/CZ Biohub/Deep Genomics) has emphasize using “AI/machine learning” for their research and product. Other relative “big” biotech startups in Silicon Valley, e.g., Verily, C
A Walkthrough of Convolutional Neural Network — Hyperparameter Tuning
Among the diverse deep learning architecture, convolutional neural network (convnets for short) stands out for its unprecedented performance on computer vision. It is an artificial neural network inspired by the animal visual cortex and has been sucessfully applied to vision recognition tasks. In this article, I will provide a walkthrough on convnet, how to tune hyperparameters and how to visualiz
3D Convolutional Neural Network input shape
I'm having a problem feeding a 3D CNN using Keras and Python to classify 3D shapes. I have a folder with some models in JSON format. I read those models into a Numpy Array. The models are 25*25*25 and represent the occupancy grid of the voxelized model (each position represents if the voxel in position (i,j,k) has points in it or no), so I only have 1 channel of input, like grayscale images in 2D
Spectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network
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Robust Visual Tracking With Deep Convolutional Neural Network Based Object Proposals on PETS
Robust Visual Tracking with Deep Convolutional Neural Network based Object Proposals on PETS Gao Zhu1 Fatih Porikli1,2,3 Hongdong Li1,3 Australian National University1 , NICTA2 ARC Centre of Excellence for Robotic Vision3 {gao.zhu,fatih.porikli,hongdong.li}@anu.edu.au ∗ Abstract Tracking by detection based object tracking methods en- counter numerous complications including object appear- ance cha
Convolutional LSTM Network の ライブラリ実装状況 〜 空間座標の位置特徴量 と 時間軸の時系列特徴量 を 両方、学習させる deep neural network モデル - Qiita
実用価値の高いCNN畳み込み層とLSTMを組み合わせたモデルです。 論文 では、地理空間の各座標地点ごと の 時系列降水量 分析タスク を 扱っています。 GIS地図エリアごとの時系列変化グラフ を 出す こと ができるため、降水量分析問題 以外にも、マーケティング商圏分析 から、無人自動車支援のための交通流量分析 など、広範な用途 が 考えられます。 【 原論文 】 Xingjian Shi, Zhourong Chen, Hao Wang and Dit-Yan Yeung, Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting 【 関連Webページ 】 masataka46さん Qiita記事 「LSTMを改良してconvLSTMにする」 convLSTMについて co
Tube Convolutional Neural Network (T-CNN) for Action Detection in Videos
Deep learning has been demonstrated to achieve excellent results for image classification and object detection. However, the impact of deep learning on video analysis (e.g. action detection and recognition) has been limited due to complexity of video data and lack of annotations. Previous convolutional neural networks (CNN) based video action detection approaches usually consist of two major steps
Deep Convolutional Neural Network Inference with Floating-point Weights and Fixed-point Activations
Deep convolutional neural network (CNN) inference requires significant amount of memory and computation, which limits its deployment on embedded devices. To alleviate these problems to some extent, prior research utilize low precision fixed-point numbers to represent the CNN weights and activations. However, the minimum required data precision of fixed-point weights varies across different network
Apache MXNet
1.9.1 master 1.9.1 1.8.0 1.7.0 1.6.0 1.5.0 1.4.1 1.3.1 1.2.1 1.1.0 1.0.0 0.12.1 0.11.0
Convolutional Neural Tensor Network Architecture for Community-based Question Answering
Convolutional Neural Tensor Network Architecture for Community-based Question Answering Xipeng Qiu and Xuanjing Huang Shanghai Key Laboratory of Data Science, Fudan University School of Computer Science, Fudan University 825 Zhangheng Road, Shanghai, China xpqiu@fudan.edu.cn, xjhuang@fudan.edu.cn Abstract Retrieving similar questions is very important in community-based question answering. A major
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A convolutional neural network approach for acoustic scene classification
GitHub - saikatbsk/Vincent-AI-Artist: Style transfer using deep convolutional neural nets
GitHub - ArunMichaelDsouza/tensorflow-image-detection: A generic image detection program that uses Google's Machine Learning library, Tensorflow and a pre-trained Deep Learning Convolutional Neural Network model called Inception.
GitHub - RyosukeHonda/Behavioral-Cloning: Use Convolutional neural network to clone human driving behavior in the game
GitHub - ichiroex/chainer-cnn: convolutional neural network for sentence classification by Chainer
MetalImageRecognition: Performing Image Recognition with Inception_v3 Network using Metal Performance Shaders Convolutional Neural Network routines
GitHub - lpigou/Theano-3D-ConvNet: 3D convolutional neural network using Theano. A CNN for video; a spatio-temporal classifier.
