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Semi-supervised Learning for Real-world Object Recognition using Adversarial Autoencoders SUDHANSHU MITTAL
| Content Provider | Semantic Scholar |
|---|---|
| Author | Mittal, Sudhanshu |
| Copyright Year | 2018 |
| Abstract | For many real-world applications, labeled data can be costly to obtain. Semi-supervised learning methods make use of substantially available unlabeled data along with few labeled samples. Most of the latest work on semi-supervised learning for image classification show performance on standard machine learning datasets like MNIST, SVHN, etc. In this work, we propose a convolutional adversarial autoencoder architecture for real-world data. We demonstrate the application of this architecture for semi-supervised object recognition. We show that our approach can learn from limited labeled data and outperform fully-supervised CNN baseline method by about 4% on real-world datasets. We also achieve competitive performance on the MNIST dataset compared to state-of-the-art semi-supervised learning techniques. To spur research in this direction, we compiled two real-world datasets: Internet (WIS) dataset and Real-world (RW) dataset which consists of more than 20K labeled samples each, comprising of small household objects belonging to ten classes. We also show a possible application of this method for online learning in robotics. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://kth.diva-portal.org/smash/get/diva2:1171308/FULLTEXT01.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
