Towards Data Science June 4, 2026 By Muhammad Ardi

FPN Paper Walkthrough: Leveraging the Internal Pyramid

Read the original on Towards Data Science →

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 .

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at Towards Data Science.

Towards Data Science

Aug 10

SPP-Net Paper Walkthrough: Breaking the Fixed-Size Constraint

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 .

By Muhammad Ardi

arXiv Computer Vision

2d ago

Generalized Design Choices for Deepfake Detectors

arXiv:2511.21507v2 Announce Type: replace Abstract: The effectiveness of deepfake detection methods often depends less on their core design and more on implementation details such as data preprocessi...

By Lorenzo Pellegrini, Serafino Pandolfini, Davide Maltoni, Matteo Ferrara, Marco Prati, Marco Ramilli

benchmarks

arXiv AI

Jun 16

An affordable hardware-aware neural architecture search for deploying convolutional neural networks on ultra-low-power computing platforms

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.

By Andrea Mattia Garavagno, Edoardo Ragusa, Antonio Frisoli, Paolo Gastaldo

computer-vision benchmarks

arXiv AI

Aug 11

iLTM: Integrated Large Tabular Model

arXiv:2511. 15941v2 Announce Type: replace-cross Abstract: Tabular data underpins decisions across science, industry, and public services.

By David Bonet, Mar\c{c}al Comajoan Cara, Alvaro Calafell, Daniel Mas Montserrat, Alexander G. Ioannidis

rag fine-tuning

arXiv Machine Learning

Aug 3

Lightweight Neural Networks for Affordance Segmentation: Enhancement of the Decoder Module

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.

By Simone Lugani, Edoardo Ragusa, Rodolfo Zunino, Paolo Gastaldo

computer-vision robotics

arXiv Machine Learning

Jun 16

Enhancing Physics-Informed Neural Networks Through Feature Engineering

arXiv:2502. 07209v4 Announce Type: replace Abstract: Physics-Informed Neural Networks (PINNs) seek to solve partial differential equations (PDEs) with deep learning.

By Shaghayegh Fazliani, Zachary Frangella, Madeleine Udell