More Like this

1. Few-shot anomaly detection and classification through reinforced data selection with a combinatorial reward

   https://doi.org/10.1007/s10115-025-02572-6  

   Han, Xiao; Xu, Depeng; Yuan, Shuhan; Wu, Xintao (December 2025, Knowledge and Information Systems)

   Due to the scarcity of anomalies, deep anomaly detection models are typically trained in an unsupervised or semi-supervised manner, depending on the availability of a small number of labeled samples. Currently, most unsupervised approaches detect anomalies by identifying the deviant patterns from normal samples, and some semi-supervised studies also use labeled anomalies to improve performance. However, few studies have focused on how to take advantage of potential anomalies in an easily obtained and large-scale unlabeled dataset. Meanwhile, in a semi-supervised setting, although we assume there will be a small number of labeled anomalies, the task of anomaly classification is under-exploited, which is important for domain experts. In this work, we focus on the problem of anomaly detection and classification with limited labeled samples and a large number of unlabeled samples. To this end, we develop a few-shot anomaly detection and classification model based on reinforced data selection with a combinatorial reward, called FADScr. FADScr iteratively improves performance by exploring the unlabeled dataset and selects informative samples to augment the training set to enhance both anomaly detection and classification. Experimental results show that our proposed framework is able to improve the performance of anomaly detection and classification with only a few labeled samples initially. 

   more » « less
2. Semi-Supervised Few-Shot Learning for Fine-Grained Disaster Tweet Classification

   https://doi.org/10.59297/FWXE4933  

   Zou, Henry Peng; Caragea, Cornelia; Zhou, Yue; and Caragea, Doina (May 2023, Proceedings of the 20th International ISCRAM Conference)

   Radianti, Jaziar; Dokas, Ioannis; Lalone, Nicolas; Khazanchi, Deepak (Ed.)

   The shared real-time information about natural disasters on social media platforms like Twitter and Facebook plays a critical role in informing volunteers, emergency managers, and response organizations. However, supervised learning models for monitoring disaster events require large amounts of annotated data, making them unrealistic for real-time use in disaster events. To address this challenge, we present a fine-grained disaster tweet classification model under the semi-supervised, few-shot learning setting where only a small number of annotated data is required. Our model, CrisisMatch, effectively classifies tweets into fine-grained classes of interest using few labeled data and large amounts of unlabeled data, mimicking the early stage of a disaster. Through integrating effective semi-supervised learning ideas and incorporating TextMixUp, CrisisMatch achieves performance improvement on two disaster datasets of 11.2% on average. Further analyses are also provided for the influence of the number of labeled data and out-of-domain results. 

   more » « less
3. PLATINUM: Semi-Supervised Model Agnostic Meta-Learning using Submodular Mutual Information

   Li, Changbin; Kothawade, Suraj; Chen, Feng; Iyer, Rishabh (July 2022, Proceedings of Machine Learning Research)

   Few-shot classification (FSC) requires training models using a few (typically one to five) data points per class. Meta learning has proven to be able to learn a parametrized model for FSC by training on various other classification tasks. In this work, we propose PLATINUM (semi-suPervised modeL Agnostic meTa-learnIng usiNg sUbmodular Mutual information), a novel semi-supervised model agnostic meta-learning framework that uses the submodular mutual information (SMI) functions to boost the performance of FSC. PLATINUM leverages unlabeled data in the inner and outer loop using SMI functions during meta-training and obtains richer meta-learned parameterizations for meta-test. We study the performance of PLATINUM in two scenarios - 1) where the unlabeled data points belong to the same set of classes as the labeled set of a certain episode, and 2) where there exist out-of-distribution classes that do not belong to the labeled set. We evaluate our method on various settings on the miniImageNet, tieredImageNet and Fewshot-CIFAR100 datasets. Our experiments show that PLATINUM outperforms MAML and semi-supervised approaches like pseduo-labeling for semi-supervised FSC, especially for small ratio of labeled examples per class. 

   more » « less
4. Research Replication Prediction Using Weakly Supervised Learning

   https://doi.org/10.18653/v1/2020.findings-emnlp.132  

   Luo, Tianyi; Li, Xingyu; Wang, Hainan; Liu, Yang (January 2020, In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings)

   null (Ed.)

   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 achieve an accuracy of 75.76% for predicting the replicability of research. 

   more » « less
5. Crowd Counting with Minimal Data Using Generative Adversarial Networks for Multiple Target Regression

   https://doi.org/10.1109/WACV.2018.00131  

   Olmschenk, Greg; Tang, Hao; Zhu, Zhigang (March 2018, 2018 IEEE Winter Conference on Applications of Computer Vision (WACV))

   In this work, we use a generative adversarial network (GAN) to train crowd counting networks using minimal data. We describe how GAN objectives can be modified to allow for the use of unlabeled data to benefit inference training in semi-supervised learning. More generally, we explain how these same methods can be used in more generic multiple regression target semi-supervised learning, with crowd counting being a demonstrative example. Given a convolutional neural network (CNN) with capabilities equivalent to the discriminator in the GAN, we provide experimental results which show that our GAN is able to outperform the CNN even when the CNN has access to significantly more labeled data. This presents the potential of training such networks to high accuracy with little data. Our primary goal is not to outperform the state-of-the-art using an improved method on the entire dataset, but instead we work to show that through semi-supervised learning we can reduce the data required to train an inference network to a given accuracy. To this end, systematic experiments are performed with various numbers of images and cameras to show under which situations the semi-supervised GANs can improve results. 

   more » « less