ソフトウェア業界ではワクワクする新しいテクノロジーがどんどん生まれ、それが世の中で使われるまでも早くなっています。2018年に革新があった Deep Learning は、既に民主化・日常化もしてます。この講演では、そのソフトウェアの今を俯瞰し、今後どうなっていくのか? その未来予想とともに。職業として20年以上の経験を得た私の学びをお伝えします。
Learning from and teaching in communities
コミュニティーで学び、そこで教えた事
Can we bring “Software Carpentry” to Japan? 「ソフトウィア・カーペントリー」を日本でやりませんか?
Presentation in English (with slides in English and Japanese)
#TokyoR 73th Meeting 2018-10-20
Tom Kelly (RIKEN IMS, Yokohama, Japan)
ソフトウェア業界ではワクワクする新しいテクノロジーがどんどん生まれ、それが世の中で使われるまでも早くなっています。2018年に革新があった Deep Learning は、既に民主化・日常化もしてます。この講演では、そのソフトウェアの今を俯瞰し、今後どうなっていくのか? その未来予想とともに。職業として20年以上の経験を得た私の学びをお伝えします。
Learning from and teaching in communities
コミュニティーで学び、そこで教えた事
Can we bring “Software Carpentry” to Japan? 「ソフトウィア・カーペントリー」を日本でやりませんか?
Presentation in English (with slides in English and Japanese)
#TokyoR 73th Meeting 2018-10-20
Tom Kelly (RIKEN IMS, Yokohama, Japan)
A Fast Content-Based Image Retrieval Method Using Deep Visual FeaturesHiroki Tanioka
Fast and scalable Content-Based Image Retrieval using visual features is required for document analysis, Medical image analysis, etc. in the present age. Convolutional Neural Network (CNN) activations as features achieved their outstanding performance in this area. Deep Convolutional representations using the softmax function in the output layer are also ones among visual features. However, almost all the image retrieval systems hold their index of visual features on main memory in order to high responsiveness, limiting their applicability for big data applications. In this paper, we propose a fast calculation method of cosine similarity with L2 norm indexed in advance on Elasticsearch. We evaluate our approach with ImageNet Dataset and VGG-16 pre-trained model. The evaluation results show the effectiveness and efficiency of our proposed method.
Super Easy Way of Building Image Search with KerasHiroki Tanioka
This paper provides detailed suggestions to create an Image Search Engine with Deep Learning. There are still few attempts with Deep Learning on a search engine. Here is a good idea of an extremely easy way of building an image search with Elasticsearch and Keras on Jupyter Notebook. So, it is demonstrated how an image search engine can be created where Keras is used to extract features from images, and Elasticsearch is used for indexing and retrieval.