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1. Improving Label Noise Robustness with Data Augmentation and Semi-Supervised Learning (Student Abstract)

   Nishi, Kento; Ding, Yi; Rich, Alex; Höllerer, Tobias (May 2021, Proceedings of the AAAI Conference on Artificial Intelligence)

   Modern machine learning algorithms typically require large amounts of labeled training data to fit a reliable model. To minimize the cost of data collection, researchers often employ techniques such as crowdsourcing and web scraping. However, web data and human annotations are known to exhibit high margins of error, resulting in sizable amounts of incorrect labels. Poorly labeled training data can cause models to overfit to the noise distribution, crippling performance in real-world applications. In this work, we investigate the viability of using data augmentation in conjunction with semi-supervised learning to improve the label noise robustness of image classification models. We conduct several experiments using noisy variants of the CIFAR-10 image classification dataset to benchmark our method against existing algorithms. Experimental results show that our augmentative SSL approach improves upon the state-of-the-art. 

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2. A Self-Supervised Learning Pipeline for Demographically Fair Facial Attribute Classification

   Ramachandran, S; Rattani, Ajita (September 2024, IEEE International Joint Conference on Biometrics)

   Published research highlights the presence of demographic bias in automated facial attribute classification. The proposed bias mitigation techniques are mostly based on supervised learning, which requires a large amount of labeled training data for generalizability and scalability. However, labeled data is limited, requires laborious annotation, poses privacy risks, and can perpetuate human bias. In contrast, self-supervised learning (SSL) capitalizes on freely available unlabeled data, rendering trained models more scalable and generalizable. However, these label-free SSL models may also introduce biases by sampling false negative pairs, especially at low-data regimes (< 200K images) under low compute settings. Further, SSL-based models may suffer from performance degradation due to a lack of quality assurance of the unlabeled data sourced from the web. This paper proposes a fully self-supervised pipeline for demographically fair facial attribute classifiers. Leveraging completely unlabeled data pseudolabeled via pre-trained encoders, diverse data curation techniques, and meta-learning-based weighted contrastive learning, our method significantly outperforms existing SSL approaches proposed for downstream image classification tasks. Extensive evaluations on the FairFace and CelebA datasets demonstrate the efficacy of our pipeline in obtaining fair performance over existing baselines. Thus, setting a new benchmark for SSL in the fairness of facial attribute classification. 

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3. One-Shot Learning With Attention-Guided Segmentation in Cryo-Electron Tomography

   https://doi.org/10.3389/fmolb.2020.613347  

   Zhou, Bo; Yu, Haisu; Zeng, Xiangrui; Yang, Xiaoyan; Zhang, Jing; Xu, Min (January 2021, Frontiers in Molecular Biosciences)

   null (Ed.)

   Cryo-electron Tomography (cryo-ET) generates 3D visualization of cellular organization that allows biologists to analyze cellular structures in a near-native state with nano resolution. Recently, deep learning methods have demonstrated promising performance in classification and segmentation of macromolecule structures captured by cryo-ET, but training individual deep learning models requires large amounts of manually labeled and segmented data from previously observed classes. To perform classification and segmentation in the wild (i.e., with limited training data and with unseen classes), novel deep learning model needs to be developed to classify and segment unseen macromolecules captured by cryo-ET. In this paper, we develop a one-shot learning framework, called cryo-ET one-shot network (COS-Net), for simultaneous classification of macromolecular structure and generation of the voxel-level 3D segmentation, using only one training sample per class. Our experimental results on 22 macromolecule classes demonstrated that our COS-Net could efficiently classify macromolecular structures with small amounts of samples and produce accurate 3D segmentation at the same time. 

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4. Use of Commercial Satellite Imagery to Monitor Changing Arctic Polygonal Tundra

   https://doi.org/10.14358/PERS.21-00061R2  

   Hasan, Amit; Udawalpola, Mahendra; Liljedahl, Anna; Witharana, Chandi (April 2022, Photogrammetric Engineering & Remote Sensing)

   Commercial satellite sensors offer the luxury of mapping of individual permafrost features and their change over time. Deep learning convolutional neural nets (CNNs) demonstrate a remarkable success in automated image analysis. Inferential strengths ofCNNmodels are driven primarily by the quality and volume of hand-labeled training samples. Production of hand-annotated samples is a daunting task. This is particularly true for regional-scale mapping applications, such as permafrost feature detection across the Arctic. Image augmentation is a strategic data-space solution to synthetically inflate the size and quality of training samples by transforming the color space or geometric shape or by injecting noise. In this study, we systematically investigate the effectiveness of a spectrum of augmentation methods when applied toCNNalgorithms to recognize ice-wedge polygons from commercial satellite imagery. Our findings suggest that a list of augmentation methods (such as hue, saturation, and salt and pepper noise) can increase the model performance. 

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5. Text Classification Using Label Names Only: A Language Model Self-Training Approach

   https://doi.org/10.18653/v1/2020.emnlp-main.724  

   Meng, Yu; Zhang, Yunyi; Huang, Jiaxin; Xiong, Chenyan; Ji, Heng; Zhang, Chao; Han, Jiawei (January 2020, EMNLP'20: 2020 Conf. on Empirical Methods in Natural Language Processing, Nov. 2020)

   null (Ed.)

   Current text classification methods typically require a good number of human-labeled documents as training data, which can be costly and difficult to obtain in real applications. Hu-mans can perform classification without seeing any labeled examples but only based on a small set of words describing the categories to be classified. In this paper, we explore the potential of only using the label name of each class to train classification models on un-labeled data, without using any labeled documents. We use pre-trained neural language models both as general linguistic knowledge sources for category understanding and as representation learning models for document classification. Our method (1) associates semantically related words with the label names, (2) finds category-indicative words and trains the model to predict their implied categories, and (3) generalizes the model via self-training. We show that our model achieves around 90% ac-curacy on four benchmark datasets including topic and sentiment classification without using any labeled documents but learning from unlabeled data supervised by at most 3 words (1 in most cases) per class as the label name1. 

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