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Researchers from the University of Oxford Developed a Deep Learning-Based Software for Precision Tracking of Fish Movement in Complex Environments

Marktechpost

Addressing these challenges, a UK-based research team introduced a hybrid method, merging deep learning and traditional computer vision techniques to enhance tracking accuracy for fish in complex experiments. The deep learning part involves the use of object detection and tracking.

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UCLA Researchers Propose PhyCV: A Physics-Inspired Computer Vision Python Library

Marktechpost

Artificial intelligence is making noteworthy strides in the field of computer vision. One key area of development is deep learning, where neural networks are trained on huge datasets of images to recognize and classify objects, scenes, and events. All Credit For This Research Goes To the Researchers on This Project.

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Microsoft Releases GRIN MoE: A Gradient-Informed Mixture of Experts MoE Model for Efficient and Scalable Deep Learning

Marktechpost

Artificial intelligence (AI) research has increasingly focused on enhancing the efficiency & scalability of deep learning models. These models have revolutionized natural language processing, computer vision, and data analytics but have significant computational challenges.

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How to Visualize Deep Learning Models

The MLOps Blog

Deep learning models are typically highly complex. While many traditional machine learning models make do with just a couple of hundreds of parameters, deep learning models have millions or billions of parameters. The reasons for this range from wrongly connected model components to misconfigured optimizers.

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A New AI Research Proposes VanillaNet: A Novel Neural Network Architecture Emphasizing the Elegance and Simplicity of Design while Retaining Remarkable Performance in Computer Vision Tasks

Marktechpost

ResNet expands on this achievement by including identity mappings through shortcut connections, enabling the training of deep neural networks with good performance across various computer vision applications, including image classification, object identification, and semantic segmentation.

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Why Deep Learning is Always Done on Array Data? New AI Research Introduces ‘Spatial Functa,’ Where From Data to Functa is Treated Like One

Marktechpost

Recently, neural fields have gained a lot of traction in computer vision as a means of representing signals like pictures, 3D shapes/scenes, movies, music, medical images, and weather data. Prior functa work demonstrated that deep learning on neural fields is possible for many different modalities, even with relatively small datasets.

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How Can Automated Retail Checkouts Recognize Unlabeled Produce? Meet the PseudoAugment Computer Vision Approach

Marktechpost

With the advancements in machine learning and deep learning techniques, there has also been an increase in automation of various dimensions. To overcome this problem, researchers from Skoltech and other institutions have devised a new way to distinguish weighted goods at a supermarket.