GeoPos: A Minimal Positional Encoding for Enhanced Fine-Grained Details in Image Synthesis Using Convolutional Neural Networks
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866915048485552128 |
|---|---|
| author | Hosseini, Mehran Hosseini, Peyman |
| author_facet | Hosseini, Mehran Hosseini, Peyman |
| contents | The enduring inability of image generative models to recreate intricate geometric features, such as those present in human hands and fingers has been an ongoing problem in image generation for nearly a decade. While strides have been made by increasing model sizes and diversifying training datasets, this issue remains prevalent across all models, from denoising diffusion models to Generative Adversarial Networks (GAN), pointing to a fundamental shortcoming in the underlying architectures. In this paper, we demonstrate how this problem can be mitigated by augmenting convolution layers geometric capabilities through providing them with a single input channel incorporating the relative n-dimensional Cartesian coordinate system. We show this drastically improves quality of images generated by Diffusion Models, GANs, and Variational AutoEncoders (VAE). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_01951 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GeoPos: A Minimal Positional Encoding for Enhanced Fine-Grained Details in Image Synthesis Using Convolutional Neural Networks Hosseini, Mehran Hosseini, Peyman Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning 51 I.2.10; I.4.0; I.4.10 The enduring inability of image generative models to recreate intricate geometric features, such as those present in human hands and fingers has been an ongoing problem in image generation for nearly a decade. While strides have been made by increasing model sizes and diversifying training datasets, this issue remains prevalent across all models, from denoising diffusion models to Generative Adversarial Networks (GAN), pointing to a fundamental shortcoming in the underlying architectures. In this paper, we demonstrate how this problem can be mitigated by augmenting convolution layers geometric capabilities through providing them with a single input channel incorporating the relative n-dimensional Cartesian coordinate system. We show this drastically improves quality of images generated by Diffusion Models, GANs, and Variational AutoEncoders (VAE). |
| title | GeoPos: A Minimal Positional Encoding for Enhanced Fine-Grained Details in Image Synthesis Using Convolutional Neural Networks |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning 51 I.2.10; I.4.0; I.4.10 |
| url | https://arxiv.org/abs/2401.01951 |