MIT-Adobe FiveK dataset
MIT-Adobe FiveK Dataset Summary We collected 5,000 photographs taken with SLR cameras by a set of different photographers. They are all in RAW format; that is, all the information recorded by the camera sensor is preserved. We made sure that these photographs cover a broad range of scenes, subjects, and lighting conditions. We then hired five photography students in an art school to adjust the ton
Food-101 -- Mining Discriminative Components with Random Forests
Food-101 – Mining Discriminative Components with Random Forests Lukas Bossard, Matthieu Guillaumin, Luc Van Gool In this paper we address the problem of automatically recognizing pictured dishes. To this end, we introduce a novel method to mine discriminative parts using Random Forests (RF), which allows us to mine for parts simultaneously for all classes and to share knowledge among them. To impr
WaveNet: A Generative Model for Raw Audio | DeepMind
Research WaveNet: A generative model for raw audio Published 8 September 2016 Authors Aäron van den Oord, Sander Dieleman This post presents WaveNet, a deep generative model of raw audio waveforms. We show that WaveNets are able to generate speech which mimics any human voice and which sounds more natural than the best existing Text-to-Speech systems, reducing the gap with human performance by ove
Sketch Simplification・ラフスケッチの自動線画化
Neural Network-based Sketch Simplification全層畳込みニューラルネットワークによるラフスケッチの自動線画化 Edgar Simo-Serraシモセラ エドガー*, Satoshi Iizuka飯塚里志*, Kazuma Sasaki佐々木一真, Hiroshi Ishikawa石川博 (*equal contribution筆頭著者に相当) Project Siteプロジェクトサイト Regarding this service本サービスについて We provide a service to use AI to clean rough sketches based on "Learning to Simplify: Fully Convolutional Networks for Rough Sketch Cleanup" [Simo-Serra
Synthesizing the preferred inputs for neurons in neural networks via deep generator networks
Deep neural networks (DNNs) have demonstrated state-of-the-art results on many pattern recognition tasks, especially vision classification problems. Understanding the inner workings of such computational brains is both fascinating basic science that is interesting in its own right - similar to why we study the human brain - and will enable researchers to further improve DNNs. One path to understan
Google supercharges machine learning tasks with TPU custom chip | Google Cloud Blog
Google supercharges machine learning tasks with TPU custom chip Editor's Update June 27, 2017: We recently announced Cloud TPUs. Machine learning provides the underlying oomph to many of Google’s most-loved applications. In fact, more than 100 teams are currently using machine learning at Google today, from Street View, to Inbox Smart Reply, to voice search. But one thing we know to be true at Goo
シモセラ エドガー ラフスケッチの自動線画化
本研究では、畳込みニューラルネットワークを用いてラフスケッチを線画に自動変換する手法を提案する。既存のスケッチ簡略化手法の多くは単純なラフスケッチのベクター画像のみを対象としており、スキャンした鉛筆画など、ラスター形式の複雑なラフスケッチを線画化するのは困難であった。これに対し提案手法では、3種類の畳込み層から構成されるニューラルネットワークモデルによって複雑なラフと線画の対応を学習することで、ラスター形式の様々なラフスケッチを良好に線画化することができる。提案モデルでは、任意のサイズやアスペクト比をもつ画像を入力として扱うことが可能であり、出力される線画は入力画像と同じサイズになる。また、このような多層構造をもつモデルを学習させるため、ラフスケッチと線画がペアになった新しいデータセットを構築し、モデルを効果的に学習させる方法を提案した。得られた結果についてユーザテストを行い、提案手法の性
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
We consider image transformation problems, where an input image is transformed into an output image. Recent methods for such problems typically train feed-forward convolutional neural networks using a \emph{per-pixel} loss between the output and ground-truth images. Parallel work has shown that high-quality images can be generated by defining and optimizing \emph{perceptual} loss functions based o
Colorful Image Colorization
Example input grayscale photos and output colorizations from our algorithm. These examples are cases where our model works especially well. For randomly selected examples, see the Performance comparisons section below. Welcome! Computer vision algorithms often work well on some images, but fail on others. Ours is like this too. We believe our work is a significant step forward in solving the color
Multi-Scale Context Aggregation by Dilated Convolutions
State-of-the-art models for semantic segmentation are based on adaptations of convolutional networks that had originally been designed for image classification. However, dense prediction and image classification are structurally different. In this work, we develop a new convolutional network module that is specifically designed for dense prediction. The presented module uses dilated convolutions t
U-Net: Convolutional Networks for Biomedical Image Segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation The u-net is convolutional network architecture for fast and precise segmentation of images. Up to now it has outperformed the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. It has won the Grand Challenge for Computer-Automated De
ImageWorsener
ImageWorsener is a cross-platform command-line utility and library for image scaling and other image processing. It has full support for PNG, JPEG, BMP formats, experimental support for WebP, read-only support for GIF, and limited support for some other image formats. It’s not as fast or memory-efficient as some utilities, but it’s very accurate. It is free software, licensed under an MIT-style li
COCO - Common Objects in Context
What is COCO? COCO is a new image recognition, segmentation, and captioning dataset. COCO has several features: Object segmentation Recognition in Context Multiple objects per image More than 300,000 images More than 2 Million instances 80 object categories 5 captions per image Keypoints on 100,000 people Tsung-Yi Lin Cornell Tech Genevieve Patterson MSR Matteo Ruggero Ronchi Caltech Yin Cui Corne
Net2Net: Accelerating Learning via Knowledge Transfer
We introduce techniques for rapidly transferring the information stored in one neural net into another neural net. The main purpose is to accelerate the training of a significantly larger neural net. During real-world workflows, one often trains very many different neural networks during the experimentation and design process. This is a wasteful process in which each new model is trained from scra
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Google Play Books introduces Bubble Zoom
Deeply-Recursive Convolutional Network for Image Super-Resolution
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
Princeton ModelNet
2015年12月PRMU研究会 対応点探索のための特徴量表現
Best practice for migrating an existing Git repo to support LFS? · Issue #326 · git-lfs/git-lfs
