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

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

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

There are a huge number of features which are said to improve Convolutional Neural Network (CNN) accuracy. Practical testing of combinations of such features on large datasets, and theoretical justification of the result, is required. Some features operate on certain models exclusively and for certain problems exclusively, or only for small-scale datasets; while some features, such as batch-normalization and residual-connections, are applicable to the majority of models, tasks, and datasets. We assume that such universal features include Weighted-Residual-Connections (WRC), Cross-Stage-Partial-connections (CSP), Cross mini-Batch Normalization (CmBN), Self-adversarial-training (SAT) and Mish-activation. We use new features: WRC, CSP, CmBN, SAT, Mish activation, Mosaic data augmentation, CmBN, DropBlock regularization, and CIoU loss, and combine some of them to achieve state-of-the-art results: 43.5% AP (65.7% AP50) for the MS COCO dataset at a realtime speed of ~65 FPS on Tesla V100. Source code is at this https URL

YOLOv4: Optimal Speed and Accuracy of Object Detection

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Review on the preparation of high value-added carbon materials from biomass

Neural Architecture Search (NAS) aims to automatically discover optimal architectures. In this paper, we propose an hourglass-inspired approach (HourNAS) for extremely fast NAS. It is motivated by the fact that the effects of the architecture often proceed from the vital few blocks. Acting like the narrow neck of an hourglass, vital blocks in the guaranteed path from the input to the output of a deep neural network restrict the information flow and influence the network accuracy. The other blocks occupy the major volume of the network and determine the overall network complexity, corresponding to the bulbs of an hourglass. To achieve an extremely fast NAS while preserving the high accuracy, we propose to identify the vital blocks and make them the priority in the architecture search. The search space of those non-vital blocks is further shrunk to only cover the candidates that are affordable under the computational resource constraints. Experimental results on ImageNet show that only using 3 hours (0.1 days) with one GPU, our HourNAS can search an architecture that achieves a 77.0% Top-1 accuracy, which outperforms the state-of-the-art methods.

/pdf/hournas-extremely-fast-neural-architecture-search-through-an-44idhs5pyq.pdf

HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens

Light-weight convolutional neural networks (CNNs) are specially designed for applications on mobile devices with faster inference speed. The convolutional operation can only capture local information in a window region, which prevents performance from being further improved. Introducing self-attention into convolution can capture global information well, but it will largely encumber the actual speed. In this paper, we propose a hardware-friendly attention mechanism (dubbed DFC attention) and then present a new GhostNetV2 architecture for mobile applications. The proposed DFC attention is constructed based on fully-connected layers, which can not only execute fast on common hardware but also capture the dependence between long-range pixels. We further revisit the expressiveness bottleneck in previous GhostNet and propose to enhance expanded features produced by cheap operations with DFC attention, so that a GhostNetV2 block can aggregate local and long-range information simultaneously. Extensive experiments demonstrate the superiority of GhostNetV2 over existing architectures. For example, it achieves 75.3% top-1 accuracy on ImageNet with 167M FLOPs, significantly suppressing GhostNetV1 (74.5%) with a similar computational cost. The source code will be available at https://github.com/huawei-noah/Efficient-AI-Backbones/tree/master/ghostnetv2_pytorch and https://gitee.com/mindspore/models/tree/master/research/cv/ghostnetv2.

GhostNetV2: Enhance Cheap Operation with Long-Range Attention

Reputation fraud campaigns (RFCs) distort the reputations of rated items, by generating fake ratings through multiple spammers. One effective way of detecting RFCs is to characterize their collective behaviors based on rating histories. However, these campaigns are constantly evolving and changing tactics to evade detection. For example, they can launch early attacks on the items to quickly dominate the reputations. They can also whitewash themselves through creating new accounts for subsequent attacks. It is thus challenging for existing approaches working on historical data to promptly react to such emerging fraud activities. In this paper, we conduct RFC detection in online fashion, so as to spot campaign activities as early as possible. This leads to a unified and scalable optimization framework, FRAUDSCAN, that can adapt to emerging fraud patterns over time. Empirical analysis on two real-world datasets validates the effectiveness and efficiency of the proposed framework.

/pdf/online-reputation-fraud-campaign-detection-in-user-ratings-3qgz6rfb2i.pdf

Online reputation fraud campaign detection in user ratings

This paper studies the collaborative budget allocation problem in which users are not isolated in the collaborative consumption of goods or services when available goods or services are limited. Different from existing methods that treat each user independently, we investigate the geometric properties of user’s consumption or preference on services, and design a matrix completion framework on the simplex. In this framework, an item’s allocation vector indicating how available services are allocated to users is estimated by the combination of user profiles as basis points on the simplex. Instead of using Euclidean distance directly, we specify a Riemannian distance on the simplex or project histogram data on simplex to Euclidean space. To intensify our model’s stability, we relax the exact recovery constraint to make a robust collaborative prediction. The resulting objective function is then efficiently optimized by a Riemannian conjugate gradient method on the simplex. Experiments on real-world data sets demonstrate our model’s competitiveness versus other collaborative budget prediction methods. Comparisons of different distance metrics for histogram data are shown and discussed. Note to Practitioners —This paper was motivated by the collaborative budget allocation (CBA) problem in which either a user has limited ratings that can be distributed among items, or a service has limited availability to all users in the sharing economy system. Existing approaches rarely consider this phenomenon in their collaborative prediction modeling, which implies the geometric constraints on a user’s rating vector. This paper suggests a matrix factorization framework to address this problem by learning a group of a user profile as basis points that can be combined to recover other users’ rating vectors. Considering the noise in observed ratings in real applications, we enhance the robustness of collaborative prediction by relaxing the recovery constraint. We then propose to specify the distance metric on the simplex from two perspectives, and give the optimization approach. In the future research, we will address the CBA in more complex settings, for example, some items can be substitutes or complementary for each other, in this way, how items can be better distributed among users is an interesting problem.

Chang Xu

Papers

Review on the preparation of high value-added carbon materials from biomass

HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens

GhostNetV2: Enhance Cheap Operation with Long-Range Attention

Online reputation fraud campaign detection in user ratings

Matrix Factorization for Collaborative Budget Allocation