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

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

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

Data Mining - Concepts and Techniques.

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

Most of distance metric learning algorithms usually learn a single distance metric over the single-view data and cannot directly exploit multi-view data. In many visual classification applications, we have access to multi-view feature representations. To exploit more discriminative information for classification, it is desired to learn several distance metrics from multi-view data. To this aim, we propose a collaborative multi-view metric learning (CMML) method for visual classification. The proposed method jointly learns multiple distance metrics under which multiple feature representations are consistent across different views, i.e., the difference of the distance metrics learned in different views is enforced to be as small as possible. Experimental results on two visual classification tasks including face recognition and scene classification show the efficacy of the CMML method.

Collaborative multi-view metric learning for visual classification

Multi-label image classification aims to predict multiple labels for a single image. However, the difficulties of predicting different labels may vary dramatically due to semantic variations of the label as well as the image context. Direct learning of multi-label classification models has the risk of being biased and overfitting those difficult labels, e.g., deep network based classifiers are over-trained on the difficult labels, therefore, lead to false-positive errors of those difficult labels during testing. To handle difficult labels of multi-label image classification, we propose to calibrate the model, which not only predicts the labels but also estimates the uncertainty of the prediction. With the new calibration branch of the network, the classification model is trained with the pick-all-labels normalized loss and optimized pertaining to the number of positive labels. Moreover, to improve performance on difficult labels, instead of annotating them, we leverage the calibrated model as the teacher network and teach the student network about handling difficult labels via uncertainty distillation. Our proposed uncertainty distillation teaches the student network which labels are highly uncertain through prediction distribution distillation, and locates the image regions that cause such uncertain predictions through uncertainty attention distillation. Conducting extensive evaluations on benchmark datasets, we demonstrate that our proposed uncertainty distillation is valuable to handle difficult labels of multi-label image classification.

https://dl.acm.org/doi/pdf/10.1145/3474085.3475406

Handling Difficult Labels for Multi-label Image Classification via Uncertainty Distillation

Motivated by the previous success of Two-Dimensional Convolutional Neural Network (2D CNN) on image recognition, researchers endeavor to leverage it to characterize videos. However, one limitation of applying 2D CNN to analyze videos is that different frames of a video share the same 2D CNN kernels, which may result in repeated and redundant information utilization, especially in the spatial semantics extraction process, hence neglecting the critical variations among frames. In this paper, we attempt to tackle this issue through two ways. 1) Design a sequential channel filtering mechanism, i.e., Progressive Enhancement Module (PEM), to excite the discriminative channels of features from different frames step by step, and thus avoid repeated information extraction. 2) Create a Temporal Diversity Loss (TD Loss) to force the kernels to concentrate on and capture the variations among frames rather than the image regions with similar appearance. Our method is evaluated on benchmark temporal reasoning datasets Something-Something V1 and V2, and it achieves visible improvements over the best competitor by 2.4% and 1.3%, respectively. Besides, performance improvements over the 2D-CNN-based state-of-the-arts on the large-scale dataset Kinetics are also witnessed.

Temporal Distinct Representation Learning for Action Recognition

In this chapter, a nonverbal way of communication for human–robot interaction by understanding human upper body gestures will be addressed. The human–robot interaction system based on a novel combination of sensors is proposed. It allows one person to interact with a humanoid social robot with natural body language. The robot can understand the meaning of human upper body gestures and express itself by using a combination of body movements, facial expressions, and verbal language. A set of 12 upper body gestures is involved for communication. Human–object interactions are also included in these gestures. The gestures can be characterized by the head, arm, and hand posture information. CyberGlove II is employed to capture the hand posture. This feature is combined with the head and arm posture information captured from Microsoft Kinect. This is a new sensor solution for human-gesture capture. Based on the body posture data, an effective and real-time human gesture recognition method is proposed. For experiments, a human body gesture dataset was built. The experimental results demonstrate the effectiveness and efficiency of the proposed approach.

Body Movement Analysis and Recognition

Graph convolutional networks (GCN) have recently demonstrated their potential in analyzing non-grid structure data that can be represented as graphs. The core idea is to encode the local topology of a graph, via convolutions, into the feature of a center node. In this paper, we propose a novel GCN model, which we term as Shortest Path Graph Attention Network (SPAGAN). Unlike conventional GCN models that carry out node-based attentions within each layer, the proposed SPAGAN conducts path-based attention that explicitly accounts for the influence of a sequence of nodes yielding the minimum cost, or shortest path, between the center node and its higher-order neighbors. SPAGAN therefore allows for a more informative and intact exploration of the graph structure and further {a} more effective aggregation of information from distant neighbors into the center node, as compared to node-based GCN methods. We test SPAGAN on the downstream classification task on several standard datasets, and achieve performances superior to the state of the art. Code is publicly available at this https URL.

Junsong Yuan

Papers

Collaborative multi-view metric learning for visual classification

Handling Difficult Labels for Multi-label Image Classification via Uncertainty Distillation

Temporal Distinct Representation Learning for Action Recognition

Body Movement Analysis and Recognition

SPAGAN: Shortest Path Graph Attention Network.