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Weakly Supervised Multilabel Clustering and its Applications in Computer Vision [PDF]
, 2016 Clustering is a useful statistical tool in computer vision and machine learning. It is generally accepted that introducing supervised information brings remarkable performance improvement to clustering.
Hong, Richang +6 more
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From Weakly Supervised Learning to Active Learning
CoRR, 2022 Applied mathematics and machine computations have raised a lot of hope since the recent success of supervised learning. Many practitioners in industries have been trying to switch from their old paradigms to machine learning. Interestingly, those data scientists spend more time scrapping, annotating and cleaning data than fine-tuning models.
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Deepometry, a framework for applying supervised and weakly supervised deep learning to imaging cytometry [PDF]
, 2021 Deep learning offers the potential to extract more than meets the eye from images captured by imaging flow cytometry. This protocol describes the application of deep learning to single-cell images to perform supervised cell classification and weakly ...
Claire Barnes, Paul Rees
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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 +2 more
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Learning Weakly Supervised Multimodal Phoneme Embeddings [PDF]
Interspeech 2017, 2017 Recent works have explored deep architectures for learning multimodal speech representation (e.g. audio and images, articulation and audio) in a supervised way. Here we investigate the role of combining different speech modalities, i.e. audio and visual information representing the lips movements, in a weakly supervised way using Siamese networks and ...
Chaabouni, Rahma +3 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 +5 more
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Array, 2023 Due to the emergence of social networking sites and social media platforms, there is faster information dissemination to the public. Unverified information is widely disseminated across social media platforms without any apprehension about the accuracy ...
Liyakathunisa Syed +3 more
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Instance-Aware Plant Disease Detection by Utilizing Saliency Map and Self-Supervised Pre-Training
Agriculture, 2022 Plant disease detection is essential for optimizing agricultural productivity and crop quality. With the recent advent of deep learning and large-scale plant disease datasets, many studies have shown high performance of supervised learning-based plant ...
Taejoo Kim +3 more
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Consistent prototype contrastive learning for weakly supervised person search [PDF]
Weakly supervised person search simultaneously addresses detection and re-identification tasks without relying on person identity labels. Prototype-based contrastive learning is commonly used to address unsupervised person re-identification.
Yu, Xiaohan +4 more
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Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data [PDF]
, 2010 In this paper, we present an overview of generalized expectation criteria (GE) , a simple, robust, scalable method for semi-supervised training using weakly-labeled data.
Mann, GS, McCallum, A
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