Knowing whether a published research result can be replicated is important. Carrying out direct replication of published research incurs a high cost. There are efforts tried to use machine learning aided methods to predict scientific claims’ replicability. However, existing machine learning aided approaches use only hand-extracted statistics features such as p-value, sample size, etc. without utilizing research papers’ text information and train only on a very small size of annotated data without making the most use of a large number of unlabeled articles. Therefore, it is desirable to develop effective machine learning aided automatic methods which can automatically extract text information as features so that we can benefit from Natural Language Processing techniques. Besides, we aim for an approach that benefits from both labeled and the large number of unlabeled data. In this paper, we propose two weakly supervised learning approaches that use automatically extracted text information of research papers to improve the prediction accuracy of research replication using both labeled and unlabeled datasets. Our experiments over real-world datasets show that our approaches obtain much better prediction performance compared to the supervised models utilizing only statistic features and a small size of labeled dataset. Further, we are able to achievemore »
VideoSSL: Semi-Supervised Learning for Video Classiﬁcation
We propose a semi-supervised learning approach for video classiﬁcation, VideoSSL, using convolutional neural networks (CNN). Like other computer vision tasks, existing supervised video classiﬁcation methods demand a large amount of labeled data to attain good performance. However, annotation of a large dataset is expensive and time consuming. To minimize the dependence on a large annotated dataset, our proposed semi-supervised method trains from a small number of labeled examples and exploits two regulatory signals from unlabeled data. The ﬁrst signal is the pseudo-labels of unlabeled examples computed from the conﬁdences of the CNN being trained. The other is the normalized probabilities, as predicted by an image classiﬁer CNN, that captures the information about appearances of the interesting objects in the video. We show that, under the supervision of these guiding signals from unlabeled examples, a video classiﬁcation CNN can achieve impressive performances utilizing a small fraction of annotated examples on three publicly available datasets: UCF101, HMDB51, and Kinetics.
- Award ID(s):
- Publication Date:
- NSF-PAR ID:
- Journal Name:
- Winter Conference on Applications of Computer Vision (WACV), 2021.
- Sponsoring Org:
- National Science Foundation
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