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

As visual recognition scales up to ever larger numbers of categories, maintaining high accuracy is increasingly difficult. Embodiment of the present invention include methods for optimizing accuracy-specificity trade-offs in large scale recognition where object categories form a semantic hierarchy consisting of many levels of abstraction.

Method and System for Optimizing Accuracy-Specificity Trade-offs in Large Scale Visual Recognition

Glaucoma is the leading cause of preventable, irreversible blindness world-wide. The disease can remain asymptomatic until severe, and an estimated 50%-90% of people with glaucoma remain undiagnosed. Thus, glaucoma screening is recommended for early detection and treatment. A cost-effective tool to detect glaucoma could expand healthcare access to a much larger patient population, but such a tool is currently unavailable. We trained a deep learning (DL) algorithm using a retrospective dataset of 58,033 images, assessed for gradability, glaucomatous optic nerve head (ONH) features, and referable glaucoma risk. The resultant algorithm was validated using 2 separate datasets. For referable glaucoma risk, the algorithm had an AUC of 0.940 (95%CI, 0.922-0.955) in validation dataset "A" (1,205 images, 1 image/patient; 19% referable where images were adjudicated by panels of fellowship-trained glaucoma specialists) and 0.858 (95% CI, 0.836-0.878) in validation dataset "B" (17,593 images from 9,643 patients; 9.2% referable where images were from the Atlanta Veterans Affairs Eye Clinic diabetic teleretinal screening program using clinical referral decisions as the reference standard). Additionally, we found that the presence of vertical cup-to-disc ratio >= 0.7, neuroretinal rim notching, retinal nerve fiber layer defect, and bared circumlinear vessels contributed most to referable glaucoma risk assessment by both glaucoma specialists and the algorithm. Algorithm AUCs ranged between 0.608-0.977 for glaucomatous ONH features. The DL algorithm was significantly more sensitive than 6 of 10 graders, including 2 of 3 glaucoma specialists, with comparable or higher specificity relative to all graders. A DL algorithm trained on fundus images alone can detect referable glaucoma risk with higher sensitivity and comparable specificity to eye care providers.

Deep Learning to Assess Glaucoma Risk and Associated Features in Fundus Images.

Visual Census: Using Cars to Study People and Society

Can cell phones be used to detect earthquakes? The Community Seismic Network (CSN) is building a dense sensor network from inexpensive and community owned sensors, such as cell phones and USB accelerometers. Detecting rare events such as earthquakes is a difficult sensing problem, and is compounded by the wide variations among sensors in a heterogeneous community network. We demonstrate an end-to-end system using Android cell phones and a cloud fusion center that allows participants to create “mock earthquakes”. Upon detecting such an event, the cloud fusion center issues real-time alerts to the phones. A map-based interface to the fusion center is projected nearby, displaying the information reported by the phones.

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Jonathan Krause

Papers

Method and System for Optimizing Accuracy-Specificity Trade-offs in Large Scale Visual Recognition

Deep Learning to Assess Glaucoma Risk and Associated Features in Fundus Images.

Visual Census: Using Cars to Study People and Society

Demo abstract, the next big one: Detecting earthquakes and other rare events from community-based sensors

Assisted reads for diabetic retinopathy using a deep learning algorithm and integrated gradient explanation