Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers—8× deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

/pdf/deep-residual-learning-for-image-recognition-b75jg61qrq.pdf

Deep Residual Learning for Image Recognition

In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.

Very Deep Convolutional Networks for Large-Scale Image Recognition

Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers---8x deeper than VGG nets but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. 
The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Deep Learning

룸의 레이아웃을 추정하기 위한 시스템들 및 방법들이 개시된다. 룸 레이아웃은 바닥, 하나 이상의 벽들 및 천장의 위치를 포함한다. 일 양상에서, 뉴럴 네트워크는 룸 레이아웃을 결정하기 위해 룸의 부분의 이미지를 분석할 수 있다. 뉴럴 네트워크는 인코더 서브-네트워크, 디코더 서브-네트워크 및 사이드 서브-네트워크를 갖는 콘볼루션 뉴럴 네트워크를 포함할 수 있다. 뉴럴 네트워크는 룸 타입과 연관된 2-차원 순서화된 키포인트들을 사용하여 3-차원 룸 레이아웃을 결정할 수 있다. 룸 레이아웃은 증강 또는 혼합 현실, 로봇공학, 자율 실내 내비게이션 등과 같은 애플리케이션들에서 사용될 수 있다.

룸 레이아웃 추정 방법들 및 기술들

본 개시내용은 네트워크가 해결하고자 하는 문제/데이터의 상관관계들(correlations)을 이용함으로써 신경 네트워크들의 구조 학습을 구현하기 위한 개선된 접근법을 제공한다. 하단 콘볼루셔널 층들로부터 완전히 연결된 층들까지 내내 정보 이득의 병목들을 발견하는 그리디 접근법이 설명된다. 단순히 아키텍처를 더 깊게 하기 보다는, 부가적인 컴퓨테이션 및 커패시턴스는 그것이 필요할 곳에만 부가된다.

콘볼루셔널 신경 네트워크들에서의 구조 학습

Systems and methods for estimating a gaze vector of an eye using a trained neural network. An input image of the eye may be received from a camera. The input image may be provided to the neural network. Network output data may be generated using the neural network. The network output data may include two-dimensional (2D) pupil data, eye segmentation data, and/or cornea center data. The gaze vector may be computed based on the network output data. The neural network may be previously trained by providing a training input image to the neural network, generating training network output data, receiving ground-truth (GT) data, computing error data based on a difference between the training network output data and the GT data, and modifying the neural network based on the error data.

Method and system for performing eye tracking using an off-axis camera

La presente invention concerne un systeme de recherche a reconnaissance faciale qui identifie un ou plusieurs noms possibles (ou autres identifiants personnels) correspondant a l'image ou aux images faciales d'une requete, comme suit. Apres avoir recu la requete visuelle contenant une ou plusieurs images faciales, le systeme identifie des images qui pourraient eventuellement correspondre a l'image faciale respective selon des criteres de similarite visuelle. Ensuite, une ou plusieurs personnes associees aux images potentielles sont identifiees. Pour chaque personne identifiee, des donnees specifiques a la personne comprenant des metriques de connectivite sociale par rapport au demandeur, sont recuperees d'une pluralite d'applications, telles que des applications de communication, des applications de reseautage social, des applications de calendrier, et des applications collaboratives. Une liste classee de personnes est ensuite generee en classant les personnes identifiees selon au moins des metriques de similarite visuelle entre l'image faciale respective et les correspondances d'images potentielles et les metriques de connexion sociale. Enfin, au moins un identifiant de la liste est envoye au demandeur.

Reconnaissance faciale avec assistance de réseau social

이미지에 기초하여 신경 네트워크(neural network)에 대한 입력들을 생성하기 위한 방법은, 이미지를 수신하는 단계, 이미지 내의 포지션을 식별하는 단계, 및 포지션에서 이미지의 서브세트를 식별하는 단계를 포함한다 이미지의 서브세트는 코너들의 제1 세트에 의해 정의된다 이 방법은 또한, 코너들의 제2 세트를 형성하도록 코너들의 제1 세트 중 적어도 하나를 섭동시키는 단계를 포함한다 코너들의 제2 세트는 이미지의 수정된 서브세트를 정의한다 이 방법은, 이미지의 서브세트와 이미지의 수정된 서브세트 간의 비교에 기초하여 호모그래피(homography)를 결정하는 단계, 호모그래피를 이미지에 적용함으로써 변환된 이미지를 생성하는 단계, 및 포지션에서 변환된 이미지의 서브세트를 식별하는 단계를 더 포함한다

Andrew Rabinovich

Papers

룸 레이아웃 추정 방법들 및 기술들

콘볼루셔널 신경 네트워크들에서의 구조 학습

Method and system for performing eye tracking using an off-axis camera

Reconnaissance faciale avec assistance de réseau social

콘볼루셔널 이미지 변환 추정을 수행하기 위한 방법 및 시스템