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.

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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

Masking effect of non-predictable energy of image regions

Spatially adaptive nonlocal patch-wise estimation is one of the most promising recent directions in image processing. Within this framework a set of the state-of-the-art Block Matching 3-D (BM3D) algorithms has been developed for different imaging problems [1]–[5]. Recently, a special prior hoas been proposed allowing to reformulate the multi-state hard-thresholding BM3D denoising as global minimization of an energy criterion [6]. The out-standing performance of BM3D works as a strong argument in favor of this prior giving an efficient multilayer redundant image model. The variational formulation is used in [6] in order to design a novel recursive denoising algorithm. In this paper the nonlocal collaborative l 0 -norm prior is a tool to design deblurring algorithms, where the global penalty function works as an adaptive regularizator. The main contribution concerns the development and testing of algebraic and frequency domain recursive algorithms minimizing the global criterion. Simulation demonstrate a very good performance of the novel algorithms.

Nonlocal image deblurring: Variational formulation with nonlocal collaborative L 0 -norm prior

Video inpainting of complex scenes based on local statistical model.

Broadband hyperspectral digital holography and Fourier transform spectroscopy are important instruments in various science and application fields. In the digital hyperspectral holography and spectroscopy the variable of interest are obtained as inverse discrete cosine transforms of observed diffractive intensity patterns. In these notes, we provide a variety of algorithms for the inverse cosine transform with the proofs of perfect spectrum reconstruction, as well as we discuss and illustrate some nontrivial features of these algorithms.

Hyperspectral holography and spectroscopy: computational features of inverse discrete cosine transform

A priori knowledge on noise characteristics is important for image processing applications in remote sensing. Although their estimation is sometimes possible in an interactive mode, blind estimation is becoming actual nowadays. For radar images, one needs to accurately estimate speckle variance. In this paper, we propose a blind and fully automatic method to perform such an estimation. One of the advantages of this method is that it is applicable for spatially correlated speckle often taking place in practice. The proposed method is applicable for multichannel data processing since it analyzes data in blocks of difference images. We have verified the proposed method on dual-polarization Sentinel-1 radar images and have obtained the results of high accuracy.

Karen Egiazarian

Papers

Masking effect of non-predictable energy of image regions

Nonlocal image deblurring: Variational formulation with nonlocal collaborative L 0 -norm prior

Video inpainting of complex scenes based on local statistical model.

Hyperspectral holography and spectroscopy: computational features of inverse discrete cosine transform

Blind estimation of speckle characteristics for sentinel polarimetric radar images