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.

[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。

Kaggle CIFAR-10の話 - デー

http://ultraist.hatenablog.com/entry/2014/11/08/192646

• 学習データを36倍に増化(Data Augmentation)
• GCN + ZCA Whiteningで正規化
• VGGのモデルをベースにしたConvolutional Neural Network(CNN)を学習
• 上記のモデルを重みの初期値とMini-Batch-SGDの更新順を変えて6個学習し、各分類器の平均を予測として出力