We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.

/pdf/going-deeper-with-convolutions-1yobw2o2ds.pdf

Going deeper with convolutions

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

Despite the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper convolutional neural networks, one central problem remains largely unsolved: how do we recover the finer texture details when we super-resolve at large upscaling factors? The behavior of optimization-based super-resolution methods is principally driven by the choice of the objective function. Recent work has largely focused on minimizing the mean squared reconstruction error. The resulting estimates have high peak signal-to-noise ratios, but they are often lacking high-frequency details and are perceptually unsatisfying in the sense that they fail to match the fidelity expected at the higher resolution. In this paper, we present SRGAN, a generative adversarial network (GAN) for image super-resolution (SR). To our knowledge, it is the first framework capable of inferring photo-realistic natural images for 4x upscaling factors. To achieve this, we propose a perceptual loss function which consists of an adversarial loss and a content loss. The adversarial loss pushes our solution to the natural image manifold using a discriminator network that is trained to differentiate between the super-resolved images and original photo-realistic images. In addition, we use a content loss motivated by perceptual similarity instead of similarity in pixel space. Our deep residual network is able to recover photo-realistic textures from heavily downsampled images on public benchmarks. An extensive mean-opinion-score (MOS) test shows hugely significant gains in perceptual quality using SRGAN. The MOS scores obtained with SRGAN are closer to those of the original high-resolution images than to those obtained with any state-of-the-art method.

/pdf/photo-realistic-single-image-super-resolution-using-a-2y0438rlmd.pdf

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

Intra prediction with fine directions is a critical feature in the new High Efficiency Video Coding (HEVC) standard because it provides significant performance gain. Different from the intra prediction in the H.264/AVC, this approach is more complicated in terms of computation and memory access, which makes the VLSI design very difficult. In this paper, we propose an efficient uniform architecture for all of the 4×4 intra directional modes. The architecture is implemented by a register array and a flexible reference sample selection technique. This novel architecture does not need to project the samples from the side reference to the main reference. Thus, it reduces the processing latency and the number of registers considerably. The proposed architecture has been implemented with TSMC 0.13μm CMOS technology. Simulation results show that the proposed architecture only needs 9020 logic gates for 17 directional modes and can run at 150 MHz operation frequency.

An efficient VLSI architecture for 4×4 intra prediction in the High Efficiency Video Coding (HEVC) standard

Immune memory clonal selection algorithms for designing stack filters

The reconstruction of a high resolution (HR) image from its low resolution (LR) counterpart is a challenging problem.
The recently developed sparse representation (SR) techniques provide new solutions to this inverse problem by
introducing the l1-norm sparsity prior into the super-resolution reconstruction process. In this paper, we present a new SR
based image super-resolution by optimizing the objective function under an adaptive sparse domain and with the
nonlocal regularization of the HR images. The adaptive sparse domain is estimated by applying principal component
analysis to the grouped nonlocal similar image patches. The proposed objective function with nonlocal regularization can
be efficiently solved by an iterative shrinkage algorithm. The experiments on natural images show that the proposed
method can reconstruct HR images with sharp edges from degraded LR images.

/pdf/super-resolution-with-nonlocal-regularized-sparse-2yjr9780aq.pdf

Super-resolution with nonlocal regularized sparse representation

Blind image quality assessment (BIQA) is a useful but challenging task. It is a promising idea to design BIQA methods by mimicking the working mechanism of human visual system (HVS). The internal generative mechanism (IGM) indicates that the HVS actively infers the primary content (i.e., meaningful information) of an image for better understanding. Inspired by that, this paper presents a novel BIQA metric by mimicking the active inference process of IGM. Firstly, an active inference module based on the generative adversarial network (GAN) is established to predict the primary content, in which the semantic similarity and the structural dissimilarity (i.e., semantic consistency and structural completeness) are both considered during the optimization. Then, the image quality is measured on the basis of its primary content. Generally, the image quality is highly related to three aspects, i.e., the scene information (content-dependency), the distortion type (distortion-dependency), and the content degradation (degradation-dependency). According to the correlation between the distorted image and its primary content, the three aspects are analyzed and calculated respectively with a multi-stream convolutional neural network (CNN) based quality evaluator. As a result, with the help of the primary content obtained from the active inference and the comprehensive quality degradation measurement from the multi-stream CNN, our method achieves competitive performance on five popular IQA databases. Especially in cross-database evaluations, our method achieves significant improvements.

Guangming Shi

Papers

An efficient VLSI architecture for 4×4 intra prediction in the High Efficiency Video Coding (HEVC) standard

Immune memory clonal selection algorithms for designing stack filters

Super-resolution with nonlocal regularized sparse representation

Blind Image Quality Assessment With Active Inference

Fully convolutional measurement network for compressive sensing image reconstruction