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.

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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

Therapeutic constructs with imaging modalities hold great promise for improving the treatment efficacy for cancer and many other diseases. We report here the design and synthesis of a self-assembling prodrug (SAPD) by the direct linkage of camptothecin (CPT), an anticancer drug, to a metal-chelating agent, DOTA. We found that under physiological conditions the DOTA-conjugated CPT prodrug can self-assemble into tubular supramolecular polymers (SPs) with a length of several micrometers. Our studies also suggest that the resultant assemblies were stable in biological environments and exhibited a fast drug release rate in the presence of intracellular glutathione. Furthermore, the SAPD exhibited remarkable in vitro efficacy against various cancer cell lines and effectively inhibited the growth of tumor spheroids. We believe that the design and optimization of self-assembling theranostic conjugates could provide a robust yet simple platform for the development of new imaging-guided drug delivery systems.

Theranostic supramolecular polymers formed by the self-assembly of a metal-chelating prodrug

Manga is a world popular comic form originated in Japan, which typically employs black-and-white stroke lines and geometric exaggeration to describe humans' appearances, poses, and actions. In this paper, we propose MangaGAN, the first method based on Generative Adversarial Network (GAN) for unpaired photo-to-manga translation. Inspired by how experienced manga artists draw manga, MangaGAN generates the geometric features of manga face by a designed GAN model and delicately translates each facial region into the manga domain by a tailored multi-GANs architecture. For training MangaGAN, we construct a new dataset collected from a popular manga work, containing manga facial features, landmarks, bodies, and so on. Moreover, to produce high-quality manga faces, we further propose a structural smoothing loss to smooth stroke-lines and avoid noisy pixels, and a similarity preserving module to improve the similarity between domains of photo and manga. Extensive experiments show that MangaGAN can produce high-quality manga faces which preserve both the facial similarity and a popular manga style, and outperforms other related state-of-the-art methods.

Unpaired Photo-to-manga Translation Based on The Methodology of Manga Drawing

Electric, pneumatic and hydraulic tools are widely used in industry. Repetitive tasks using heavy tools would often cause fatigue for workers leading to musculoskeletal injuries. The efficiency and manufacturing quality can hardly be guaranteed either. Powered exoskeletons for upper limb [1-3] and lower limb assistance [4-5] have been developed recently.

Design and evaluation of an upper extremity wearable robot with payload balancing for human augmentation

In this paper, we propose a probabilistic scene model using object frames, each of which is a group of co-occurring objects with fixed spatial relations. In contrast to standard co-occurrence models, which mostly explore the pairwise co-existence of objects, the proposed model captures the spatial relationship among groups of objects. Such information is closely tied to the semantics of the underlying scenes, which allows us to perform object detection and scene recognition in a unified framework. The proposed probabilistic model has two major components. The first models the dependencies between object frames and objects by adopting the Latent Dirichlet Allocation model for text analysis. The second component characterizes the dependencies between object frames and scenes by establishing a mapping between global image features and object frame distributions. Experimental results show that the induced object frames are both semantically meaningful and spatially consistent. In addition, our model significantly improves the performance of object recognition and scene retrieval.

Probabilistic modeling of scenes using object frames

Wearable robots are designed to assist people, thus their operation is based on a highly dynamic human–robot interaction. In order to make this interaction as effective as possible, the design of an exoskeleton is subject to challenging requirements involving its mechanical components prior to the control algorithms. Actuator, transmission, and wearable structure play a crucial role in determining the performance of a wearable robot. Current high-performance exoskeletons leverage a new actuation paradigm, so-called quasi-direct drive (QDD) actuation, to enhance a safe and compliant behavior without renouncing a high torque density. This chapter goes through the principles behind this new enabling technology and provides demonstrations of its beneficial application on several wearable robots. Three diverse exoskeletal prototypes are showcased, which are designed to assist hip, knee, and back, respectively. Preliminary tests executed on healthy subjects confirm the impactful potential of the QDD actuation scheme: all the devices take advantage of low mechanical impedance, high backdrivability, and high torque to exhibit accurate force tracking during various activities.

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Hao Su

Papers

Theranostic supramolecular polymers formed by the self-assembly of a metal-chelating prodrug

Unpaired Photo-to-manga Translation Based on The Methodology of Manga Drawing

Design and evaluation of an upper extremity wearable robot with payload balancing for human augmentation

Probabilistic modeling of scenes using object frames

High-performance soft wearable robots for human augmentation and gait rehabilitation