ClickBAIT: Click-based Accelerated Incremental Training of Convolutional Neural Networks

Autor:	Teng, Ervin, Falcão, João Diogo, Iannucci, Bob
Rok vydání:	2017
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Artificial Intelligence Computer Science - Human-Computer Interaction C.1.3
Druh dokumentu:	Working Paper
Popis:	Today's general-purpose deep convolutional neural networks (CNN) for image classification and object detection are trained offline on large static datasets. Some applications, however, will require training in real-time on live video streams with a human-in-the-loop. We refer to this class of problem as Time-ordered Online Training (ToOT) - these problems will require a consideration of not only the quantity of incoming training data, but the human effort required to tag and use it. In this paper, we define training benefit as a metric to measure the effectiveness of a sequence in using each user interaction. We demonstrate and evaluate a system tailored to performing ToOT in the field, capable of training an image classifier on a live video stream through minimal input from a human operator. We show that by exploiting the time-ordered nature of the video stream through optical flow-based object tracking, we can increase the effectiveness of human actions by about 8 times. Comment: 11 pages, 14 figures. Datasets available at http://clickbait.crossmobile.info
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1709.05021 Zobrazit plný text záznamu View this record from Arxiv