畳み込みニューラルネットワーク - Wikipedia
畳み込みニューラルネットワーク（たたみこみニューラルネットワーク、英: Convolutional neural network、略称: CNNまたはConvNet）は層間を共通重みの局所結合で繋いだニューラルネットワークの総称・クラスである。機械学習、特に音声言語翻訳[1]や画像や動画認識に広く使われる。 CNNは、その重み（行列の）共有構造と並進不変特性に基づいて、シフト不変（shift invariant）あるいは位置不変（space invariant）人工ニューラルネットワーク（SIANN）とも呼ばれている[2][3]。 一般的な畳み込み処理は以下のように定式化される[4]。はj番目の出力チャネルを、は相互相関関数を意味する。 すなわち各出力チャネル ごとに入力チャネル 枚分の畳み込みカーネル が用意され、カーネルを用いた各入力チャネルの畳み込みの総和へバイアス項 が付与され各チ
Image Colorization Using CNN With OpenCV
Sometimes technology enhances art. Sometimes it vandalizes it. Colorizing black and white films is an ancient idea dating back to 1902. For decades many movie creators opposed the idea of colorizing their black-and-white movies and considered it vandalism of their art. Today it is accepted as an enhancement to the art form. The technology itself has moved from painstaking hand colorization to toda
Transformable Convolutional Neural Network for Text Classification | IJCAI
Convolutional neural networks (CNNs) have shown their promising performance for natural language processing tasks, which extract n-grams as features to represent the input. However, n-gram based CNNs are inherently limited to fixed geometric structure and cannot proactively adapt to the transformations of features. In this paper, we propose two modules to provide CNNs with the flexibility for comp
Visualizing convolutional neural network protein-ligand scoring - PubMed
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LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation
LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation Tak-Wai Hui, Xiaoou Tang, and Chen Change Loy CUHK-SenseTime Joint Lab, The Chinese University of Hong Kong IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018, Spotlight Presentation, Salt Lake City, Utah Abstract FlowNet2, the state-of-the-art convolutional neural network (CNN) for optical flow
[TensorFlowチュートリアル] A Guide to TF Layers: Building a Convolutional Neural Network [日本語翻訳] - Qiita
[TensorFlowチュートリアル] A Guide to TF Layers: Building a Convolutional Neural Network [日本語翻訳]PythonDeepLearningCNNTensorFlowMNIST 本記事はTensorFlowの公式チュートリアルを翻訳したものです。 オリジナルはこちら google翻訳を使用しているため不自然な箇所があるかもしれません。 日本語として不自然な箇所、語句は適宜修正していきます。 気になる箇所や誤っている箇所がありましたらご指摘いただけると幸いです。 TensorFlowは、ニューラルネットワークの構築を容易にする高水準のAPIを提供します。 全結合レイヤーやConvolutionalレイヤーの作成、アクティベーション機能の追加、ドロップアウトの正規化の適用を容易にするメソッドを提供します。 このチュートリ
![[TensorFlowチュートリアル] A Guide to TF Layers: Building a Convolutional Neural Network [日本語翻訳] - Qiita](https://cdn-ak-scissors.b.st-hatena.com/image/square/0753e965b52c9a10a72c89041160cca3646bfa0e/height=288;version=1;width=512/https%3A%2F%2Fqiita-user-contents.imgix.net%2Fhttps%253A%252F%252Fcdn.qiita.com%252Fassets%252Fpublic%252Farticle-ogp-background-9f5428127621718a910c8b63951390ad.png%3Fixlib%3Drb-4.0.0%26w%3D1200%26mark64%3DaHR0cHM6Ly9xaWl0YS11c2VyLWNvbnRlbnRzLmltZ2l4Lm5ldC9-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%26mark-x%3D142%26mark-y%3D112%26blend64%3DaHR0cHM6Ly9xaWl0YS11c2VyLWNvbnRlbnRzLmltZ2l4Lm5ldC9-dGV4dD9peGxpYj1yYi00LjAuMCZ3PTYxNiZ0eHQ9JTQwdGtpbmpvMSZ0eHQtY29sb3I9JTIzMjEyMTIxJnR4dC1mb250PUhpcmFnaW5vJTIwU2FucyUyMFc2JnR4dC1zaXplPTM2JnR4dC1hbGlnbj1sZWZ0JTJDdG9wJnM9ZDhmNjg1Mjk1YzljMDAxODJhM2IxNGY0NTdlOTA5OWI%26blend-x%3D142%26blend-y%3D491%26blend-mode%3Dnormal%26s%3D50107137c99b9c4642f6303316b292e5)
Bayesian Convolutional Neural Network with MCMC (using Tensorflow and Edward)
Bayesian Convolutional Neural Network with MCMC (using Tensorflow and Edward) import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data import os import edward as ed from edward.models import Bernoulli, Normal, Categorical,Empirical from edward.util import Progbar from keras.layers import Dense from scipy.misc import imsave import matplotlib.pyplot as plt from edward.util
Automatic Spine Segmentation using Convolutional Neural Network via Redundant Generation of Class Labels for 3D Spine Modeling
There has been a significant increase from 2010 to 2016 in the number of people suffering from spine problems. The automatic image segmentation of the spine obtained from a computed tomography (CT) image is important for diagnosing spine conditions and for performing surgery with computer-assisted surgery systems. The spine has a complex anatomy that consists of 33 vertebrae, 23 intervertebral dis
Towards Accurate Binary Convolutional Neural Network
We introduce a novel scheme to train binary convolutional neural networks (CNNs) -- CNNs with weights and activations constrained to {-1,+1} at run-time. It has been known that using binary weights and activations drastically reduce memory size and accesses, and can replace arithmetic operations with more efficient bitwise operations, leading to much faster test-time inference and lower power cons
物理サーバを選定する際のポイント – Eureka Engineering – Medium
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Learning to Extract Semantic Structure from Documents Using Multimodal Fully Convolutional Neural Network
We present an end-to-end, multimodal, fully convolutional network for extracting semantic structures from document images. We consider document semantic structure extraction as a pixel-wise segmentation task, and propose a unified model that classifies pixels based not only on their visual appearance, as in the traditional page segmentation task, but also on the content of underlying text. Moreove
Hyperparameters Optimization in Deep Convolutional Neural Network / Bayesian Approach with Gaussian Process Prior
Convolutional Neural Network is known as ConvNet have been extensively used in many complex machine learning tasks. However, hyperparameters optimization is one of a crucial step in developing ConvNet architectures, since the accuracy and performance are reliant on the hyperparameters. This multilayered architecture parameterized by a set of hyperparameters such as the number of convolutional laye
CrescendoNet: A Simple Deep Convolutional Neural Network with Ensemble Behavior
We introduce a new deep convolutional neural network, CrescendoNet, by stacking simple building blocks without residual connections. Each Crescendo block contains independent convolution paths with increased depths. The numbers of convolution layers and parameters are only increased linearly in Crescendo blocks. In experiments, CrescendoNet with only 15 layers outperforms almost all networks witho
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Dilution (neural networks) - Wikipedia
https://keras.io/layers/convolutional/
AAAS
GitHub - DeNA/SRCNNKit: CoreML and Keras implementation of Super-Resolution Convolutional Neural Network (SRCNN)
DeepLearning.Hub on Twitter: "A Survey on Acceleration of Deep Convolutional Neural Networks: - Network Pruning - Low-rank Approximation - Networ… https://t.co/3LavPh5fLV"
http://twitter.com/i/web/status/959361214005493760
Regularization and Optimization strategies in Deep Convolutional Neural Network