mlsanityData to 7 Oct 2026

Artistic Neural Style Transfer Algorithms with Activation Smoothing

arXiv 2411.08014 ↗First posted 12 Nov 2024cs.CV eess.IVcited by 3

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Abstract (arXiv)

The works of Gatys et al. demonstrated the capability of Convolutional Neural Networks (CNNs) in creating artistic style images. This process of transferring content images in different styles is called Neural Style Transfer (NST). In this paper, we re-implement image-based NST, fast NST, and arbitrary NST. We also explore to utilize ResNet with activation smoothing in NST. Extensive experimental results demonstrate that smoothing transformation can greatly improve the quality of stylization results.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 11 of its 21 references that are in the map. First posted on arXiv 12 Nov 2024.

Builds on

11 of 21 references.

References · 11

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  2. 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,678
  3. 2015A Neural Algorithm of Artistic Stylecited by 480
  4. 2016A Learned Representation For Artistic Stylecited by 340
  5. 2021Rethinking and Improving the Robustness of Image Style Transfercited by 48
  6. 2024TD3 Based Collision Free Motion Planning for Robot Navigationcited by 18
  7. 2024YOLO-PPA based Efficient Traffic Sign Detection for Cruise Control in Autonomous Drivingcited by 16
  8. 2024Autonomous Navigation of Unmanned Vehicle Through Deep Reinforcement Learningcited by 12
  9. 2024Machine Learning-Based Research on the Adaptability of Adolescents to Online Educationcited by 11
  10. 2024Research on Improved U-net Based Remote Sensing Image Segmentation Algorithmcited by 10
  11. 2024DRAL: Deep Reinforcement Adaptive Learning for Multi-UAVs Navigation in Unknown Indoor Environmentcited by 10

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Citing papers per year: 2024: 2; 2025: 1.

3 papers.