pix2pixHD
ComboLoss
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| pix2pixHD | ComboLoss | |
|---|---|---|
| 6 | 1 | |
| 6,515 | 30 | |
| 0.8% | - | |
| 0.0 | 3.6 | |
| 11 months ago | over 3 years ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | MIT License |
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pix2pixHD
- How do I run more than 200 epochs in training a Pix2PixHD model?
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NVIDIA DLSS Now Available in Over 150 Games, Including Dying Light 2 Stay Human, Sifu and Phantasy Star Online 2 New Genesis
Well, maybe not, considering things like pix2pix can generate detail from just solid shapes and colors.
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Image to hand drawn
Sources: U2Net, ArtLine, Pix2PixHD, APDrawingGAN
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[P] I made FaceShop! Instance segmentation + CGAN for editing faces (badly)
Pix2PixHD (from DeepSIM)
Uses a mix of instance segmentation (BiSeNet) and conditional GAN, and is heavily inspired by the Pix2PixHD and DeepSIM papers. Will have more details when I wake up!
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How to access a class object when I use torch.nn.DataParallel()?
I used Pix2PixHD implementation in GitHub if you want to see the full code.
ComboLoss
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[D] Could this network be used to generate the most attractive image possible? What would it look like... -"ComboLoss for Facial Attractiveness Analysis with Squeeze-and-Excitation Networks"
Abstract: Loss function is crucial for model training and feature representation learning, conventional models usually regard facial attractiveness recognition task as a regression problem, and adopt MSE loss or Huber variant loss as supervision to train a deep convolutional neural network (CNN) to predict facial attractiveness score. Little work has been done to systematically compare the performance of diverse loss functions. In this paper, we firstly systematically analyze model performance under diverse loss functions. Then a novel loss function named ComboLoss is proposed to guide the SEResNeXt50 network. The proposed method achieves state-of-the-art performance on SCUT-FBP, HotOrNot and SCUT-FBP5500 datasets with an improvement of 1.13%, 2.1% and 0.57% compared with prior arts, respectively. Code and models are available at this https URL.
What are some alternatives?
pytorch-CycleGAN-and-pix2pix - Image-to-Image Translation in PyTorch
pytorch-metric-learning - The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.
awesome-colab-notebooks - Collection of google colaboratory notebooks for fast and easy experiments
d2l-en - Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
sofgan - [TOG 2022] SofGAN: A Portrait Image Generator with Dynamic Styling
jina - ☁️ Build multimodal AI applications with cloud-native stack
face-parsing.PyTorch - Using modified BiSeNet for face parsing in PyTorch
generative-inpainting-pytorch - A PyTorch reimplementation for paper Generative Image Inpainting with Contextual Attention (https://arxiv.org/abs/1801.07892)
contrastive-unpaired-translation - Contrastive unpaired image-to-image translation, faster and lighter training than cyclegan (ECCV 2020, in PyTorch)
CycleGAN - Software that can generate photos from paintings, turn horses into zebras, perform style transfer, and more.
StyleSwin - [CVPR 2022] StyleSwin: Transformer-based GAN for High-resolution Image Generation
Im2Vec - [CVPR 2021 Oral] Im2Vec Synthesizing Vector Graphics without Vector Supervision