Learning to Answer Questions From Image Using Convolutional Neural Network
In this paper, we propose to employ the convolutional neural network (CNN) for the image question answering (QA). Our proposed CNN provides an end-to-end framework with convolutional architectures for learning not only the image and question representations, but also their inter-modal interactions to produce the answer. More specifically, our model consists of three CNNs: one image CNN to encode t
ABC-CNN: An Attention Based Convolutional Neural Network for Visual Question Answering
We propose a novel attention based deep learning architecture for visual question answering task (VQA). Given an image and an image related natural language question, VQA generates the natural language answer for the question. Generating the correct answers requires the model's attention to focus on the regions corresponding to the question, because different questions inquire about the attributes
Chess position evaluation with convolutional neural network in Julia
In this post we will try to challenge the problem of chess position evaluation using convolutional neural network (CNN) – a neural network type designed to deal with spatial data. We will first explain why we need CNNs then we will present two fundamental CNN layers. Having some knowledge from the inside of the black box, we will apply CNN to the binary classification problem of chess position eva
Convolutional Neural Networkを実装する - Qiita
Deep Learning系のライブラリを試すのが流行っていますが、Exampleを動かすのはいいとしても、いざ実際のケースで使おうとするとうまくいかないことがよくあります。 なんとか動かしてみたけれど精度が出ない、データの加工の仕方が悪いのか、モデルのパラメーターが悪いのか、原因がぜんぜんわからん・・・という事態を乗り越えるには、やはり仕組みに対する理解が必要になってきます。 そんなわけで、本編では画像の用意という一番最初のスタートラインから、Chainerで実装したCNNを学習させるところまで、行うべき手順とその理由を解説していきたいと思います。 前段として理論編を書いていますが、ここではライブラリなどで設定しているパラメーターが、理論編の側とどのようにマッチするのかについても見ていきたいと思います。 なお、今回紹介するノウハウは下記リポジトリにまとめています。画像認識を行う際に役立て
Convolutional Neural Networkとは何なのか - Qiita
Deleted articles cannot be recovered. Draft of this article would be also deleted. Are you sure you want to delete this article? 機械学習の世界において、画像といえばConvolutional Neural Network(以下CNN)というのは、うどんといえば香川くらい当たり前のこととして認識されています。しかし、そのCNNとは何なのか、という解説は意外と少なかったりします。 そこで、本記事ではCNNについてその仕組みとメリットの解説を行っていきたいと思います。 なお、参考文献にも記載の通り解説の内容はStanfordのCNNの講座をベースにしています。こちらの講座はNeural NetworkからCNN、はてはTensorflowによる実装まで解説される予定な
What do channels refer to in a convolutional neural network?
Answer (1 of 6): Imagine a network as a sequence of "layers", where each layer is of the form x_{n+1} = f(x_n), where f(x) is a linear transformation followed by a non-linearity such as sigmoid, tanh or relu. The layers operate on 3-D chunks of data, where the first two dimensions are (generally)...
3D Visualization of a Convolutional Neural Network
Input image: Filter: Weighted input: Calculation: Output: Draw your number here Downsampled drawing: First guess: Second guess: Layer visibility Input layer Convolution layer 1 Downsampling layer 1 Convolution layer 2 Downsampling layer 2 Fully-connected layer 1 Fully-connected layer 2 Output layer Made by Adam Harley. Project details.
A Convolutional Neural Network for Modelling Sentences
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pages 655–665, Baltimore, Maryland, USA, June 23-25 2014. c 2014 Association for Computational Linguistics A Convolutional Neural Network for Modelling Sentences Nal Kalchbrenner Edward Grefenstette {nal.kalchbrenner, edward.grefenstette, phil.blunsom}@cs.ox.ac.uk Department of Computer Science University of
Modelling, Visualising and Summarising Documents with a Single Convolutional Neural Network
Capturing the compositional process which maps the meaning of words to that of documents is a central challenge for researchers in Natural Language Processing and Information Retrieval. We introduce a model that is able to represent the meaning of documents by embedding them in a low dimensional vector space, while preserving distinctions of word and sentence order crucial for capturing nuanced se
Encoding Source Language with Convolutional Neural Network for Machine Translation
The recently proposed neural network joint model (NNJM) (Devlin et al., 2014) augments the n-gram target language model with a heuristically chosen source context window, achieving state-of-the-art performance in SMT. In this paper, we give a more systematic treatment by summarizing the relevant source information through a convolutional architecture guided by the target information. With differen
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Image Colorization Using a Deep Convolutional Neural Network
A Convolutional Neural Network Neutrino Event Classifier
How do I derive the back-propagation algorithm in Convolutional Neural Network?
http://vision.stanford.edu/teaching/cs231b_spring1415/slides/alexnet_tugce_kyunghee.pdf
http://www.vision.cs.chubu.ac.jp/MPRG/F_group/F146_yamashita2014.pdf
Convolutional Neural Network Workbench
http://www.vision.cs.chubu.ac.jp/FLABResearchArchive/Bachelor/B13/Paper/fukui.pdf
Pedestrian Detection by Deep Convolutional Neural Network
Pedestrian Detection by Deep Convolutional Neural Network
http://www.vision.cs.chubu.ac.jp/flabresearcharchive/bachelor/B13/Paper/fukui.pdf