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、畳み込みを使用しているニューラルネットワークの総称である。画像認識や動画認識、音声言語翻訳[1]、レコメンダシステム[2]、自然言語処理[3]、コンピュータ将棋[4]、コンピュータ囲碁[4]などに使用されている。 畳み込みニューラルネットワークの定義は厳密に決まっているわけではないが、画像認識の（縦, 横, 色）の2次元画像の多クラス分類の場合、以下の擬似コードで書かれるのが基本形である[5]。ここから色々なバリエーションが作られている。損失関数は交差エントロピーを使用し、パラメータは確率的勾配降下法で学習するのが基本形である。これらの偏微分は自動微分を参照。 以下の繰り返し 畳み込み層と活性化関数 最大値プーリング ベク
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
from __future__ import absolute_import from __future__ import division from __future__ import print_function # Imports import numpy as np import tensorflow as tf tf.logging.set_verbosity(tf.logging.INFO) # Our application logic will be added here if __name__ == "__main__": tf.app.run() このチュートリアルでは、Convolutionalニューラルネットワークを構築、学習、評価するためのコードを追加していきます。 完成コードはここにあります。 ##Intro to Convolutional Neural Ne
![[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/293c68b83907085aba25861320313aa3cdba4e50/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-412672c5f0600ab9a64263b751f1bc81.png%3Fixlib%3Drb-4.0.0%26w%3D1200%26mark64%3DaHR0cHM6Ly9xaWl0YS11c2VyLWNvbnRlbnRzLmltZ2l4Lm5ldC9-dGV4dD9peGxpYj1yYi00LjAuMCZ3PTk3MiZoPTM3OCZ0eHQ9JTVCVGVuc29yRmxvdyVFMyU4MyU4MSVFMyU4MyVBNSVFMyU4MyVCQyVFMyU4MyU4OCVFMyU4MyVBQSVFMyU4MiVBMiVFMyU4MyVBQiU1RCUyMEElMjBHdWlkZSUyMHRvJTIwVEYlMjBMYXllcnMlM0ElMjBCdWlsZGluZyUyMGElMjBDb252b2x1dGlvbmFsJTIwTmV1cmFsJTIwTmV0d29yayUyMCU1QiVFNiU5NyVBNSVFNiU5QyVBQyVFOCVBQSU5RSVFNyVCRiVCQiVFOCVBOCVCMyU1RCZ0eHQtYWxpZ249bGVmdCUyQ3RvcCZ0eHQtY29sb3I9JTIzMUUyMTIxJnR4dC1mb250PUhpcmFnaW5vJTIwU2FucyUyMFc2JnR4dC1zaXplPTU2JnM9OGY5YjNlMzU5NjYzYjUyZDVlNTE0ZmFiODY1NzYxZTI%26mark-x%3D142%26mark-y%3D57%26blend64%3DaHR0cHM6Ly9xaWl0YS11c2VyLWNvbnRlbnRzLmltZ2l4Lm5ldC9-dGV4dD9peGxpYj1yYi00LjAuMCZoPTc2Jnc9NzcwJnR4dD0lNDB0a2luam8xJnR4dC1jb2xvcj0lMjMxRTIxMjEmdHh0LWZvbnQ9SGlyYWdpbm8lMjBTYW5zJTIwVzYmdHh0LXNpemU9MzYmdHh0LWFsaWduPWxlZnQlMkN0b3Amcz0wMjI5YWVjOTVlZGFkYThlZTEzODg4ZmVjMjEwOTAwOQ%26blend-x%3D142%26blend-y%3D486%26blend-mode%3Dnormal%26s%3Db5b92044df7a7871467e46377309c04d)
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
Eureka EngineeringLearn about Eureka’s engineering efforts, product developments and more.
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