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1. It Takes Two Flints to Make a Fire: Multitask Learning of Neural Relation and Explanation Classifiers

   Tang, Zheng; Surdeanu, Mihai (April 2022, ArXivorg)

   We propose an explainable approach for relation extraction that mitigates the tension between generalization and explainability by jointly training for the two goals. Our approach uses a multi-task learning architecture, which jointly trains a classifier for relation extraction, and a sequence model that labels words in the context of the relation that explain the decisions of the relation classifier. We also convert the model outputs to rules to bring global explanations to this approach. This sequence model is trained using a hybrid strategy: supervised, when supervision from pre-existing patterns is available, and semi-supervised otherwise. In the latter situation, we treat the sequence model’s labels as latent variables, and learn the best assignment that maximizes the performance of the relation classifier. We evaluate the proposed approach on the two datasets and show that the sequence model provides labels that serve as accurate explanations for the relation classifier’s decisions, and, importantly, that the joint training generally improves the performance of the relation classifier. We also evaluate the performance of the generated rules and show that the new rules are great add-on to the manual rules and bring the rule-based system much closer to the neural models. 

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2. It Takes Two Flints to Make a Fire: Multitask Learning of Neural Relation and Explanation Classifiers

   https://doi.org/10.1162/coli_a_00463  

   Tang, Zheng; Surdeanu, Mihai (January 2023, Computational Linguistics)

   Abstract We propose an explainable approach for relation extraction that mitigates the tension between generalization and explainability by jointly training for the two goals. Our approach uses a multi-task learning architecture, which jointly trains a classifier for relation extraction, and a sequence model that labels words in the context of the relations that explain the decisions of the relation classifier. We also convert the model outputs to rules to bring global explanations to this approach. This sequence model is trained using a hybrid strategy: supervised, when supervision from pre-existing patterns is available, and semi-supervised otherwise. In the latter situation, we treat the sequence model’s labels as latent variables, and learn the best assignment that maximizes the performance of the relation classifier. We evaluate the proposed approach on the two datasets and show that the sequence model provides labels that serve as accurate explanations for the relation classifier’s decisions, and, importantly, that the joint training generally improves the performance of the relation classifier. We also evaluate the performance of the generated rules and show that the new rules are a great add-on to the manual rules and bring the rule-based system much closer to the neural models. 

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3. A Snapshot-based Approach for Self-supervised Feature Learning and Weakly-supervised Classification on Point Cloud Data

   https://doi.org/10.5220/0010230103990408  

   Li, Xingye; Zhu, Zhigang (January 2021, VISAPP 2021, the 16th International Conference on Computer Vision Theory and Applications)

   null (Ed.)

   Manually annotating complex scene point cloud datasets is both costly and error-prone. To reduce the reliance on labeled data, we propose a snapshot-based self-supervised method to enable direct feature learning on the unlabeled point cloud of a complex 3D scene. A snapshot is defined as a collection of points sampled from the point cloud scene. It could be a real view of a local 3D scan directly captured from the real scene, or a virtual view of such from a large 3D point cloud dataset. First the snapshots go through a self-supervised pipeline including both part contrasting and snapshot clustering for feature learning. Then a weakly-supervised approach is implemented by training a standard SVM classifier on the learned features with a small fraction of labeled data. We evaluate the weakly-supervised approach for point cloud classification by using varying numbers of labeled data and study the minimal numbers of labeled data for a successful classification. Experiments are conducted on three public point cloud datasets, and the results have shown that our method is capable of learning effective features from the complex scene data without any labels. 

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4. A correct-and-certify approach to self-supervise object pose estimators via ensemble self-training

   J. Shil; R. Talak; D. Maggio; L. Carlone (July 2023, Robotics: Science and Systems (RSS))

   Real-world robotics applications demand object pose estimation methods that work reliably across a variety of scenarios. Modern learning-based approaches require large labeled datasets and tend to perform poorly outside the training domain. Our first contribution is to develop a robust corrector module that corrects pose estimates using depth information, thus enabling existing methods to better generalize to new test domains; the corrector operates on semantic keypoints (but is also applicable to other pose estimators) and is fully differentiable. Our second contribution is an ensemble self-training approach that simultaneously trains multiple pose estimators in a self-supervised manner. Our ensemble self-training architecture uses the robust corrector to refine the output of each pose estimator; then, it evaluates the quality of the outputs using observable correctness certificates; finally, it uses the observably correct outputs for further training, without requiring external supervision. As an additional contribution, we propose small improvements to a regression-based keypoint detection architecture, to enhance its robustness to outliers; these improvements include a robust pooling scheme and a robust centroid computation. Experiments on the YCBV and TLESS datasets show the proposed ensemble self-training outperforms fully supervised baselines while not requiring 3D annotations on real data. 

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5. Self-Supervised Learning for Online Anomaly Detection in High-Dimensional Data Streams

   https://doi.org/10.3390/electronics12091971  

   Mozaffari, Mahsa; Doshi, Keval; Yilmaz, Yasin (May 2023, Electronics)

   In this paper, we address the problem of detecting and learning anomalies in high-dimensional data-streams in real-time. Following a data-driven approach, we propose an online and multivariate anomaly detection method that is suitable for the timely and accurate detection of anomalies. We propose our method for both semi-supervised and supervised settings. By combining the semi-supervised and supervised algorithms, we present a self-supervised online learning algorithm in which the semi-supervised algorithm trains the supervised algorithm to improve its detection performance over time. The methods are comprehensively analyzed in terms of computational complexity, asymptotic optimality, and false alarm rate. The performances of the proposed algorithms are also evaluated using real-world cybersecurity datasets, that show a significant improvement over the state-of-the-art results. 

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