【英語】深層学習スゴい系の表現集

英語の勉強で深層学習スゴい系の表現集を集めておこうかと思い記事にすることにした.
定期的にアップデートしていくつもり.


Convolutional Neural Networks (CNNs) and other deep networks have enabled unprecedented breakthroughs in a variety of computer vision tasks, from image clasification to object detection, semantic segmentation, image captionning, and more recently visual question answering.

CNNsや他のネットワークは, コンピュータビジョンのタスクで前例のないブレークスルーを起こしている.
コンピュータビジョンのタスクとはクラス分類からオブジェクトの特定, セマンティックセグメンテーション, キャプショニング, より最近の視覚問題の回答などである.


A number of previous works have asserted that deeper representations in a CNN capture higher-level visual constructs.
たくさんの専攻研究は, CNNのより深い表現は高レベルの視覚的な構成物を掴むと断言している.

[Grad-CAM: Visual Explanationns from Deep Networks via Gradient-based Localization]


In the last few years, the most advanced deep neural networks(DNNs) have managed to reach or even surpass human level performance on a wide range of challenging machine learning tasks, including face recognition.

[LOTS about Attacking Deep Features]


In the last few years convolutional neural networks (CNNs) have emerged as powerful image representations for various category-level recognition tasks such as object classification, scene recognition or object detection. Tha basic principles of CNNs are known 80's and the recent successes are combination of advances in GPU-based computation power together with large labelled image datasets.

[NetVLAD: CNN Architecture for Weakly Supervised Place Recognition]


Since their ground-breaking result s on image classification in recent ImageNet challenge, deep learning based methods have shined in many other computer vision tasks, including object detection and semantic segmentation. Recently, they also rekindled highly semantic tasks such as image captioning and visual question answering.

[Deep Image Retrieval: Learning global representations for image search]



Meanwhile, deep transfer learning techniques have gained considerable attention in the computer vision community. First, a deep convolutional neural network (CNN) is trained on a large labeled dataset. The convolutional layers are then used as mid-level feature extractors on a variety of computer vision tasks. Although transferring convolutional netwrok features is not a new idea, the simultaneous availability of large datasets and cheap GPU co-processors has contributed to the achievement of considerable performance gains on a variety computer vision benchmarks: "SIFT and HOG descriptors produced big performance gains a decade ago, and now deep convolutional features are providing a similar breakthrough.


A flurry of recent results indicates that image descriptors extracted from deep convolutional neural networks (CNNs) are very powerful and consistently outperform highly tuned state-of-the-art systems on a variety of visual recognition tasks. Embeddings from state-of-the-art CNNs have been applied successfully to a number of problems in computer vision.
[Learning Image Embedding using Convolutional Neural Netwroks for Improved Multi-Modal Semantics]


Deep learning has proven itself as a successful set of models for learning useful semantic representation of data.
ディープラーニングはデータの有用な意味表現を学習するための一連の流れとして証明されています.
[Deep Metric Learning Using Triplet Network]