A systematic review and analysis of deep learning-based underwater object detection
Underwater object detection is one of the most challenging research topics in computer
vision technology. The complex underwater environment makes underwater images suffer …
vision technology. The complex underwater environment makes underwater images suffer …
[HTML][HTML] Utilisation of deep learning for COVID-19 diagnosis
The COVID-19 pandemic that began in 2019 has resulted in millions of deaths worldwide.
Over this period, the economic and healthcare consequences of COVID-19 infection in …
Over this period, the economic and healthcare consequences of COVID-19 infection in …
Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks
The growing energy and performance costs of deep learning have driven the community to
reduce the size of neural networks by selectively pruning components. Similarly to their …
reduce the size of neural networks by selectively pruning components. Similarly to their …
Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning
R Desislavov, F Martínez-Plumed… - … Informatics and Systems, 2023 - Elsevier
The progress of some AI paradigms such as deep learning is said to be linked to an
exponential growth in the number of parameters. There are many studies corroborating …
exponential growth in the number of parameters. There are many studies corroborating …
Group normalization
Batch Normalization (BN) is a milestone technique in the development of deep learning,
enabling various networks to train. However, normalizing along the batch dimension …
enabling various networks to train. However, normalizing along the batch dimension …
[HTML][HTML] A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework
The rice leaves related diseases often pose threats to the sustainable production of rice
affecting many farmers around the world. Early diagnosis and appropriate remedy of the rice …
affecting many farmers around the world. Early diagnosis and appropriate remedy of the rice …
Rethinking spatiotemporal feature learning: Speed-accuracy trade-offs in video classification
Despite the steady progress in video analysis led by the adoption of convolutional neural
networks (CNNs), the relative improvement has been less drastic as that in 2D static image …
networks (CNNs), the relative improvement has been less drastic as that in 2D static image …
Demystifying parallel and distributed deep learning: An in-depth concurrency analysis
Deep Neural Networks (DNNs) are becoming an important tool in modern computing
applications. Accelerating their training is a major challenge and techniques range from …
applications. Accelerating their training is a major challenge and techniques range from …
Dense convolutional network and its application in medical image analysis
T Zhou, XY Ye, HL Lu, X Zheng, S Qiu… - BioMed Research …, 2022 - Wiley Online Library
Dense convolutional network (DenseNet) is a hot topic in deep learning research in recent
years, which has good applications in medical image analysis. In this paper, DenseNet is …
years, which has good applications in medical image analysis. In this paper, DenseNet is …
Automated plant leaf disease detection and classification using optimal MobileNet based convolutional neural networks
Agriculture is the major occupation in India and it loses 35% of the crop productivity annually
owing to plant diseases. Earlier plant disease detection is a tedious process because of …
owing to plant diseases. Earlier plant disease detection is a tedious process because of …