Understanding how FPN allows deep learning models detecting small objects and how to implement it from scratch The post FPN Paper Walkthrough: Leveraging the Internal Pyramid appeared first on Towards Data Science .
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Learn how Spatial Pyramid Pooling enables CNNs to handle any image size, with a from-scratch PyTorch implementation The post SPP-Net Paper Walkthrough: Breaking the Fixed-Size Constraint appeared first on Towards Data Science .
arXiv:2606. 16290v1 Announce Type: cross Abstract: Hardware-aware neural architecture search (HW-NAS) allows the integration of Convolutional Neural Networks (CNNs) in microcontrollers devices by automatically designing neural architectures that can fit prearranged hardware constraints.
arXiv:2511. 15941v2 Announce Type: replace-cross Abstract: Tabular data underpins decisions across science, industry, and public services.
arXiv:2607. 29473v1 Announce Type: cross Abstract: The deployment of deep neural networks for visual affordance segmentation on wearable robots poses may prove critical, due to some conflicting aspects of the problem.
arXiv:2502. 07209v4 Announce Type: replace Abstract: Physics-Informed Neural Networks (PINNs) seek to solve partial differential equations (PDEs) with deep learning.
Deep learning models have emerged in machine learning and related fields, demonstrating astonishing performance in various visual tasks. Despite their great success, however, these models are unable to fully encode intrinsic visual structures, and often ignore the spatial, topological, and semantic information contained within an image.