Bookmarks: convolutional network

Showing posts with label convolutional network. Show all posts

2015-09-24

Illustration2Vec: A Semantic Vector Representation of Illustrations

http://illustration2vec.net/papers/illustration2vec-main.pdf
http://illustration2vec.net/papers/illustration2vec-supp.pdf
http://illustration2vec.net/

Referring to existing illustrations helps novice drawers to realize their ideas. To find such helpful references from a large image collection, we first build a semantic vector representation of illustrations by training convolutional neural networks. As the proposed vector space correctly reflects the semantic meanings of illustrations, users can efficiently search for references with similar attributes. Besides the search with a single query, a semantic morphing algorithm that searches the intermediate illustrations that gradually connect two queries is proposed. Several experiments were conducted to demonstrate the effectiveness of our methods.

2015-09-10

[1508.06615] Character-Aware Neural Language Models

http://arxiv.org/abs/1508.06615

We describe a simple neural language model that relies only on character-level inputs. Predictions are still made at the word-level. Our model employs a convolutional neural network (CNN) over characters, whose output is given to a long short-term memory (LSTM) recurrent neural network language model (RNN-LM).

入力は文字、出力は単語。
単語を構成する文字の各々について、文字ごとに15次元の（分布意味）埋込みベクトルに変換して、行列 C^k を作る。
その単語の行列 C^k に対して畳み込みネットワーク (CNN) と max pooling を適用してベクトルを作る。
系列の学習は LSTM。
層の途中に highway network (HW-Net) を入れている。なくても機能するが、あれば性能が上がる。

2015-05-26

ConvNetJS MNIST demo

http://cs.stanford.edu/people/karpathy/convnetjs/demo/mnist.html

This demo trains a Convolutional Neural Network on the MNIST digits dataset in your browser, with nothing but Javascript.

手書き数字の画像データを JavaScript のプログラムで学習するデモ。
学習中の各層（conv, pool, softmax）の重みやアクティベーション値をリアルタイムで見ることができる。

karpathy/convnetjs · GitHub

https://github.com/karpathy/convnetjs

ConvNetJS is a Javascript implementation of Neural networks, together with nice browser-based demos.

ConvNetJS: Deep Learning in your browser

http://cs.stanford.edu/people/karpathy/convnetjs/

ConvNetJS is a Javascript library for training Deep Learning models (mainly Neural Networks) entirely in your browser. Open a tab and you're training. No software requirements, no compilers, no installations, no GPUs, no sweat.

2015-04-08

Learning a Deep Convolutional Network for Image Super-Resolution

http://mmlab.ie.cuhk.edu.hk/projects/SRCNN.html

Test code for SRCNN

SRCNN のソースコードがある。

[1501.00092] Image Super-Resolution Using Deep Convolutional Networks

http://arxiv.org/abs/1501.00092

The mapping is represented as a deep convolutional neural network (CNN) that takes the low-resolution image as the input and outputs the high-resolution one.

畳み込みニューラルネットを使って、低解像度の画像を入力して高解像度の画像を出力する。