Results 41 to 50 of about 76,480 (326)

Weakly supervised learning of allomorphy [PDF]

Proceedings of the First Workshop on Subword and Character Level Models in NLP, 2017
Most NLP resources that offer annotations at the word segment level provide morphological annotation that includes features indicating tense, aspect, modality, gender, case, and other inflectional information. Such information is rarely aligned to the relevant parts of the words—i.e. the allomorphs, as such annotation would be very costly.
Miikka Silfverberg, Mans Hulden
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Weakly supervised classification in high energy physics

Journal of High Energy Physics, 2017
As machine learning algorithms become increasingly sophisticated to exploit subtle features of the data, they often become more dependent on simulations.
Lucio Mwinmaarong Dery, Benjamin Nachman, Francesco Rubbo, Ariel Schwartzman   +3 more
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Instance-Level Contrastive Learning for Weakly Supervised Object Detection

Sensors, 2022
Weakly supervised object detection (WSOD) has received increasing attention in object detection field, because it only requires image-level annotations to indicate the presence or absence of target objects, which greatly reduces the labeling costs ...
Ming Zhang, Bing Zeng
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Weakly supervised causal representation learning

, 2022
Learning high-level causal representations together with a causal model from unstructured low-level data such as pixels is impossible from observational data alone. We prove under mild assumptions that this representation is however identifiable in a weakly supervised setting. This involves a dataset with paired samples before and after random, unknown
Brehmer, J., Cohen, T., De Haan, P., Lippe, P.   +3 more
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SPMF-Net: Weakly Supervised Building Segmentation by Combining Superpixel Pooling and Multi-Scale Feature Fusion

Remote Sensing, 2020
The lack of pixel-level labeling limits the practicality of deep learning-based building semantic segmentation. Weakly supervised semantic segmentation based on image-level labeling results in incomplete object regions and missing boundary information ...
Jie Chen, Fen He, Yi Zhang, Geng Sun, Min Deng   +4 more
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Learning Weakly-Supervised Contrastive Representations

, 2022
We argue that a form of the valuable information provided by the auxiliary information is its implied data clustering information. For instance, considering hashtags as auxiliary information, we can hypothesize that an Instagram image will be semantically more similar with the same hashtags. With this intuition, we present a two-stage weakly-supervised
Tsai, Yao-Hung Hubert, Li, Tianqin, Liu, Weixin, Liao, Peiyuan, Salakhutdinov, Ruslan, Morency, Louis-Philippe   +5 more
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Addressing Imbalance in Weakly Supervised Multi-Label Learning

IEEE Access, 2019
Multi-label learning has been widely used in many fields to solve the problem of assigning multiple related categories to an instance. Nevertheless, the label for each training example is assumed complete in most of the current multi-label learning ...
Fang-Fang Luo, Wen-zhong Guo, Guo-Long Chen   +2 more
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Hierarchical complementary learning for weakly supervised object localization [PDF]

Signal Processing: Image Communication, 2022
Weakly supervised object localization (WSOL) is a challenging problem which aims to localize objects with only image-level labels. Due to the lack of ground truth bounding boxes, class labels are mainly employed to train the model. This model generates a class activation map (CAM) which activates the most discriminate features.
Sabrina Narimene Benassou, Wuzhen Shi, Feng Jiang, Abdallah Benzine   +3 more
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Defect detection using weakly supervised learning

2023 IEEE International Conference on Imaging Systems and Techniques (IST), 2023
In many real-world scenarios, obtaining large amounts of labeled data can be a daunting task. Weakly supervised learning techniques have gained significant attention in recent years as an alternative to traditional supervised learning, as they enable training models using only a limited amount of labeled data. In this paper, the performance of a weakly
Sevetlidis, Vasileios, Pavlidis, George, Balaska, Vasiliki, Psomoulis, Athanasios, Mouroutsos, Spyridon, Gasteratos, Antonios   +5 more
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Medical image segmentation using deep learning: A survey

IET Image Processing, 2022
Deep learning has been widely used for medical image segmentation and a large number of papers has been presented recording the success of deep learning in the field.
Risheng Wang, Tao Lei, Ruixia Cui, Bingtao Zhang, Hongying Meng, Asoke K. Nandi   +5 more
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