Parameter-Free Style Projection for Arbitrary Image Style Transfer
Author:
Affiliation:
1. Harvard University,USA
2. Baidu Research,China
3. Austin Peay State University,USA
4. Nanyang Technological University,Singapore
5. Amazon,USA
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9745891/9746004/09746290.pdf?arnumber=9746290
Reference30 articles.
1. Learning Linear Transformations for Fast Image and Video Style Transfer
2. Semantic image synthesis with spatially-adaptive normalization;park;CVPR,2019
3. A Style-Based Generator Architecture for Generative Adversarial Networks
4. Universal style transfer via feature transforms;li;NIPS,2017
5. Order Statistics
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1. StyleStegan: Leak-free Style Transfer Based on Feature Steganography;2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC);2023-10-31
2. A Saliency-Aware Method for Arbitrary Style Transfer;2023 IEEE International Conference on Image Processing (ICIP);2023-10-08
3. Generating Artistic Images Via Few-Shot Style Transfer;2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW);2023-06-04
4. QuantArt: Quantizing Image Style Transfer Towards High Visual Fidelity;2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR);2023-06
5. Bridging the BCI illiteracy gap: a subject-to-subject semantic style transfer for EEG-based motor imagery classification;Frontiers in Human Neuroscience;2023-05-15
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