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

There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

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

计量经济分析 = Econometric analysis

In the considered hybrid diffractive imaging system, a refractive lens is arranged simultaneously with a multilevel phase mask (MPM) as a diffractive optical element (DOE) for Achromatic Extended-depth-of-field (EDoF) imaging. This paper proposes a fully differentiable image formation model that uses neural network techniques to maximize the imaging quality by optimizing MPM, digital image reconstruction algorithm, refractive lens parameters (aperture size, focal length), and distance between the MPM and sensor. Firstly, model-based numerical simulations and end-to-end joint optimization of imaging are used. A spatial light modulator (SLM) is employed in the second stage of the design to implement MPM optimized at the first stage, and the image processing is optimized experimentally using a learning-based approach. The third stage of optimization is targeted at joint optimization of the SLM phase pattern and image reconstruction algorithm in the hardware-in-the-loop (HIL) setup, which allows compensation for a mismatch between numerical modeling and the physical reality of optic and sensor. A comparative analysis of the imaging accuracy and quality using the optical parameters is presented. It is proved experimentally, first time to the best of our knowledge, that wavefront phase modulation can provide imaging of advanced quality as compared with some commercial multi-lens cameras. 

Hybrid diffractive optics (DOE & refractive lens) for broadband EDoF imaging

In this paper, we study the problem of finding minimal unconditional tests for a given table. The earlier spectral method of solving this problem is modified into a form using decision diagrams and a form using genetic algorithms. The decision diagram method extends the spectral algorithm for larger tables. The genetic algorithm can be used to find a near-optimal solution for very large tables that are intractable with the other methods.

Using a spectral technique, genetic algorithms and decision diagrams for finding unconditional table tests

Pre-requisites for smart lossy compression of noisy remote sensing images

A problem of image denoising when images are corrupted by a non-stationary noise is considered in this paper. Since in practice no a priori information on noise is available, noise statistics should be pre-estimated for image denoising. In this paper, deep convolutional neural network (CNN) based method for estimation of a map of local, patch-wise, standard deviations of noise (so-called sigma-map) is proposed. It achieves the state-of-the-art performance in accuracy of estimation of sigma-map for the case of non-stationary noise, as well as estimation of noise variance for the case of additive white Gaussian noise. Extensive experiments on image denoising using estimated sigma-maps demonstrate that our method outperforms recent CNN-based blind image denoising methods by up to 6 dB in PSNR, as well as other state-of-the-art methods based on sigma-map estimation by up to 0.5 dB, providing same time better usage flexibility. Comparison with the ideal case, when denoising is applied using ground-truth sigma-map, shows that a difference of corresponding PSNR values for most of noise levels is within 0.1-0.2 dB and does not exceeds 0.6 dB.

Learning-based Noise Component Map Estimation for Image Denoising.

In this paper, we propose a novel nonparametric approach to reconstruction of three-dimensional (3D) objects from 2D blurred and noisy observations which is a problem of computational optical sectioning. This approach is based on an approximate image formation model which takes into account depth varying nature of blur described by a matrix of shift-invariant 2D point-spread functions (PSF) of an optical system. The proposed restoration scheme incorporates the matrix regularized inverse and matrix regularized Wiener inverse algorithms in combination with a novel spatially adaptive denoising. This technique is based on special statistical rules for selection of the adaptive size and shape neighbourhood used for the local polynomial approximation of the 2D image intensity. The simulations on a phantom 3D object show efficiency of the developed approach. The objective result evaluation is presented in terms of quadratic-error criteria.

/pdf/spatially-adaptive-3d-inverse-for-optical-sectioning-1i92y64fd8.pdf

Karen Egiazarian

Papers

Hybrid diffractive optics (DOE & refractive lens) for broadband EDoF imaging

Using a spectral technique, genetic algorithms and decision diagrams for finding unconditional table tests

Pre-requisites for smart lossy compression of noisy remote sensing images

Learning-based Noise Component Map Estimation for Image Denoising.

Spatially adaptive 3D inverse for optical sectioning