Overview of SR-CNN algorithm in Azure Anomaly Detector
Author: Tony Xing (@XingGuodong), AI Platform, C + AI In the last blog “Introducing Azure Anomaly Detector API”, I didn't provide enough details on one of the algorithms. As the algorithm paper was in the publishing process. The paper was accepted by KDD 2019 for oral presentation later, and this blog serves as an overview of the SR-CNN algorithm and for more details user can always read the paper. By the way, we have a 2-minute video here. Problem definition Before we go into details, let us revisit the problem definition of time series anomaly detection. Challenges For any time-series anomaly detection system that is operating in production with a large scale, there are quite a few challenges, especially on the three areas below: 1. Lack of labels - As you can imagine, with signals generated from clients, services, and sensors every second, the huge amount of volume makes it infeasible to manually label the data. 2. Generalization - With real-world data, there are so many different types of time series with different characteristics, which make it hard to generalize and find a silver bullet to solve all the problems. Some examples can be found in the figure below. 3. Efficiency - For any online anomaly detection system, efficiency is one of the key challenges. The system is expected to have low compute cost and low latency for serving. Inspiration In the computer vision domain, there is this concept called “visual saliency detection”. Saliency is what "stands out" in a photo or scene, enabling our eye-brain to quickly focus on the most important regions, as shown in figures below. Fig. Original image Fig. The salient part of the original image When we look at the time series chart, the most dominant and stand-out part is the anomalies. This similarity is where we got the inspiration and it turned out to generate great results. Algorithm Our solution then borrowed Spectral Residual (SR) from the visual saliency detection domain, then apply CNN on the results produced by the SR model As you can see from the algorithm architecture, after SR transformation, the transformed result magnifies the anomalies and the resulting signal is easier to generalize, therefore it provides us a way to training CNN with synthetic data. Spectral Residual The spectral residual algorithm consists of three major steps: Fourier Transform to get the log amplitude spectrum Calculation of spectral residual Inverse Fourier Transform that transforms the sequence back to the spatial domain Benefits SR is unsupervised, efficient, and has good generality. The problem becomes much easier based on the output of the SR model. We can train CNN on the SR output using fully synthetic data with simple synthetic rule Randomly select several points in the saliency map and calculate the injection value to replace the original point. Result We have performed online and offline experimentation, it outperformed state-of-the-arts consistently on open datasets and internal production datasets.
Building a digital guide dog for railway passengers with impaired vision
Catching your train on time can be challenging under the best of circumstances. Trains typically only stop for a few minutes, leaving little room for mistakes. For example, at Munich Main station around 240 express trains and 510 regional trains leave from 28 platforms per day. Some trains can also be quite long, up to 346 meters (1,135 ft) for express ICE trains. It is extremely important to quickly find the correct platform and platform section, and then the door closest to a reserved seat needs to be located. This already challenging adventure becomes even more so, if a vision impairment forces a customer to rely exclusively on auditory or tactile feedback. When traveling autonomously, without assistance, it is common practice to walk along the outside of a train, continuously tapping it with a white cane, to discover opened and closed doors (figure 1). While this works in principle, this practice has limitations, both in terms of speed and reliability. We therefore partnered with DB Systel GmbH, the digital partner for all Deutsche Bahn Group companies, to build the Digital Guide Dog. This is a feasibility study based on an AI-powered smartphone application that uses computer vision, auditory and haptic feedback to guide customers to the correct platform section and train car door. In this blog post, we are sharing some of the details and unique challenges that we experienced while the AI model behind this application.
Building Image Classifiers made easy with Azure Custom Vision
In our previous blog, we outlined that Supervised machine learning (ML) models need labeled data, but majority of the data collected in the raw format lacks labels. So, the first step before building a ML model would be to get the raw data labeled by domain experts. To do so, we outlined how Doccano is an easy tool for collaborative text annotation. However, not all data that gets collected is in text format, many a times we end up with a bunch of images but the end goal is again to build a Supervised ML model using them. Like stated previously, the first step would be to tag these images with specific labels. Image tagging as well as building and even deploying either a multi-class or a multi-label classifier can be done in a few simple steps using Azure Custom Vision.
