Results 31 to 40 of about 827,210 (276)
Semantic segmentation of human oocyte images using deep neural networks
BioMedical Engineering OnLine, 2021 Background Infertility is a significant problem of humanity. In vitro fertilisation is one of the most effective and frequently applied ART methods. The effectiveness IVF depends on the assessment and selection of gametes and embryo with the highest ...
Anna Targosz +3 more
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A Comparison of the State-of-the-Art Deep Learning Platforms: An Experimental Study
Sakarya University Journal of Computer and Information Sciences, 2020 Deep learning, a subfield of machine learning, has proved its efficacy on a wide range of applications including but not limited to computer vision, text analysis and natural language processing, algorithm enhancement, computational biology, physical ...
Abdullah Talha Kabakuş
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Expressivity of Deep Neural Networks
, 2022 This review paper will appear as a book chapter in the book "Theory of Deep Learning" by Cambridge University ...
Ingo Gühring +2 more
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CONVOLUTIONAL DEEP LEARNING NEURAL NETWORK FOR STROKE IMAGE RECOGNITION: REVIEW
Вестник КазНУ. Серия математика, механика, информатика, 2021 Deep learning is one of the developing area of articial intelligence research. It includes machine learning methods based on articial neural networks. One method that has been widely used and researched in recent years is convolution neural networks (CNN)
Azhar Toilybaikyzy Tursynova +3 more
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Imbedding Deep Neural Networks
CoRR, 2022 Continuous-depth neural networks, such as Neural ODEs, have refashioned the understanding of residual neural networks in terms of non-linear vector-valued optimal control problems. The common solution is to use the adjoint sensitivity method to replicate a forward-backward pass optimisation problem.
Andrew Corbett, Dmitry Kangin
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Adversarial Robustness of Deep Convolutional Neural Network-based Image Recognition Models: A Review
Leida xuebao, 2021 Deep convolutional neural networks have achieved great success in recent years. They have been widely used in various applications such as optical and SAR image scene classification, object detection and recognition, semantic segmentation, and change ...
Hao SUN +4 more
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Deep Neural Network or Dermatologist? [PDF]
, 2019 Deep learning techniques have proven high accuracy for identifying melanoma in digitised dermoscopic images. A strength is that these methods are not constrained by features that are pre-defined by human semantics. A down-side is that it is difficult to understand the rationale of the model predictions and to identify potential failure modes. This is a
Kyle Young +4 more
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StochasticNet: Forming Deep Neural Networks via Stochastic Connectivity
IEEE Access, 2016 Deep neural networks are a branch in machine learning that has seen a meteoric rise in popularity due to its powerful abilities to represent and model high-level abstractions in highly complex data.
Mohammad Javad Shafiee +2 more
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Performance analysis of different DCNN models in remote sensing image object detection
EURASIP Journal on Image and Video Processing, 2022 In recent years, deep learning, especially deep convolutional neural networks (DCNN), has made great progress. Many researchers use different DCNN models to detect remote sensing targets. Different DCNN models have different advantages and disadvantages.
Huaijin Liu +3 more
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