Multi-task learning deep neural networks for automatic speech recognition

Chen, Dongpeng

doi:10.14711/thesis-b1514768

Cited by 7 publications

(9 citation statements)

References 89 publications

(110 reference statements)

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“…Research efforts have been made in counting mitosis cells in breast cancer, lymphocytes in breast cancer WSIs, centroblasts on follicular lymphoma WSIs, and plasma cells in bone marrow image patches 41,64,83,164 . Counting cells typically requires cell segmentation; a variety of attempts have been made for cell segmentation on breast cancer, 165–168 colon cancer, 146,169 and bladder cancer 170 . Research attempts have also been made on counting neuroendocrine tumor (NET) cells within the gastrointestinal tract and pancreas 171 .…”

Section: Clinical Tasks In Computational Histopathologymentioning

confidence: 99%

Machine learning in computational histopathology: Challenges and opportunities

Cooper

Krishnan

2023

Genes Chromosomes & Cancer

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Digital histopathological images, high-resolution images of stained tissue samples, are a vital tool for clinicians to diagnose and stage cancers. The visual analysis of patient state based on these images are an important part of oncology workflow. Although pathology workflows have historically been conducted in laboratories under a microscope, the increasing digitization of histopathological images has led to their analysis on computers in the clinic. The last decade has seen the emergence of machine learning, and deep learning in particular, a powerful set of tools for the analysis of histopathological images. Machine learning models trained on large datasets of digitized histopathology slides have resulted in automated models for prediction and stratification of patient risk. In this review, we provide context for the rise of such models in computational histopathology, highlight the clinical tasks they have found success in automating, discuss the various machine learning techniques that have been applied to this domain, and underscore open problems and opportunities.

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Section: Clinical Tasks In Computational Histopathologymentioning

confidence: 99%

Machine learning in computational histopathology: Challenges and opportunities

Cooper

Krishnan

2023

Genes Chromosomes & Cancer

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“…Alternatively, the second modality is considered as privileged information given at training time but not at test time. Most works on this direction focused on joint monocular depth estimation and semantic segmentation showing that joint training allows improving the performance of both tasks (Wang et al, 2015;Mousavian et al, 2016;Zhang et al, 2018b;Kendall et al, 2018;Chen et al, 2018d;He et al, 2021b). A multi-task guided Prediction-and-Distillation Network was designed by Xu et al (2018), where the model first predicts a set of intermediate auxiliary tasks ranging from low to high level, and then such predictions are used as multi-modal input to a multi-modal distillation module, opted at learning the final tasks.…”

Section: Sis With Additional Modalitiesmentioning

confidence: 99%

Semantic Image Segmentation: Two Decades of Research

Volpi¹,

Chidlovskii²

2022

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Semantic image segmentation (SiS) plays a fundamental role in a broad variety of computer vision applications, providing key information for the global understanding of an image. This survey is an effort to summarize two decades of research in the field of SiS, where we propose a literature review of solutions starting from early historical methods followed by an overview of more recent deep learning methods including the latest trend of using transformers. We complement the review by discussing particular cases of the weak supervision and side machine learning techniques that can be used to improve the semantic segmentation such as curriculum, incremental or self-supervised learning.State-of-the-art SiS models rely on a large amount of annotated samples, which are more expensive to obtain than labels for tasks such as image classification. Since unlabeled data is instead significantly cheaper to obtain, it is not surprising that Unsupervised Domain Adaptation (UDA) reached a broad success within the semantic segmentation community. Therefore, a second core contribution of this book is to summarize five years of a rapidly growing field, Domain Adaptation for Semantic Image Segmentation (DASiS) which embraces the importance of semantic segmentation itself and a critical need of adapting segmentation models to new environments. In addition to providing a comprehensive survey on DASiS techniques, we unveil also newer trends

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“…Moreover, the domain mismatch scenario has a low-resource problem if the target domain has only fewer data compared to the scale of the source domain data. There are also several works that tried to solve this problem, such as (Chen and Mak, 2015;Zoph et al, 2016;. Our jointly trained model is also based on this low-resource scenario.…”

Section: Joint Training and Transfer Learningmentioning

confidence: 99%

Multi-accent Speech Separation with One Shot Learning

Huang¹,

Wu²,

Lee³

2021

Proceedings of the 1st Workshop on Meta Learning and Its Applications to Natural Language Processing

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Speech separation is a problem in the field of speech processing that has been studied in full swing recently. However, there has not been much work studying a multi-accent speech separation scenario. Unseen speakers with new accents and noise aroused the domain mismatch problem which cannot be easily solved by conventional joint training methods. Thus, we applied MAML and FOMAML to tackle this problem and obtained higher average Si-SNRi values than joint training on almost all the unseen accents. This proved that these two methods do have the ability to generate well-trained parameters for adapting to speech mixtures of new speakers and accents. Furthermore, we found out that FOMAML obtains similar performance compared to MAML while saving a lot of time.

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Multi-task learning deep neural networks for automatic speech recognition

Cited by 7 publications

References 89 publications

Machine learning in computational histopathology: Challenges and opportunities

Machine learning in computational histopathology: Challenges and opportunities

Semantic Image Segmentation: Two Decades of Research

Multi-accent Speech Separation with One Shot Learning

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