coral-cnn
contrastive-unpaired-translation
| coral-cnn | contrastive-unpaired-translation | |
|---|---|---|
| 4 | 6 | |
| 330 | 2,114 | |
| 2.1% | - | |
| 0.0 | 2.1 | |
| about 3 years ago | 9 months ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 or later |
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coral-cnn
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[D] Why is Ordinal Regression so overlooked?
The most recent and usable DL attempt I have found is the CORAL/CORN frameworks (keras, pytorch) which have just a few stars, and that's it.
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[D] can regression models be used for ranking?
To your question, there are specific types of models called ordinal regression / ordinal classification models that do not assume a metric distance between values. E.g., if you have "20/hr, $15/hr, $0/hr" these models don't assume that the distance between 0 and 15 is 3x the distance between 20 and 15. It just assumes 20 > 15 > 0. We worked on this a bit in the context of neural networks: https://www.sciencedirect.com/science/article/pii/S016786552030413X , https://raschka-research-group.github.io/coral_pytorch/
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[D] Modeling class errors
If you are interested, I recently worked on a simple ordinal regression approach for neural networks here: https://www.sciencedirect.com/science/article/pii/S016786552030413X
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[R] [D] What machine learning methods can be used for ordinal regression?
Just took a quick look at that paper, it sounds like a good approach. If you are interested, we recently developed an ordinal regression approach with implementation in PyTorch (https://github.com/Raschka-research-group/coral-cnn). Someone also recently ported it to Keras: https://github.com/ck37/coral-ordinal. I haven't read the paper you mentioned in detail, but it seems our method is similar except that we add the probabilities that are >0.5 and that we have theoretical guarantees. rank consistency.
contrastive-unpaired-translation
- Help me with my GANS project
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[D] What is current SOTA in Image to Image Translation?
For unpaired image-to-image translation, the SOTA is probably Contrastive Unpaired Translation, which is developed by the same group that developed CycleGAN and is kind of the successor algorithm.
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[P] Can't finish my master's thesis. What to do?
Here is link number 1 - Previous text "CUT"
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[D] What are techniques for few shot domain adaptation?
Code for https://arxiv.org/abs/2007.15651 found: https://github.com/taesungp/contrastive-unpaired-translation
- [D] What's the successor to pix2pixHD
What are some alternatives?
coral-ordinal - Tensorflow Keras implementation of ordinal regression using consistent rank logits (CORAL) by Cao et al. (2019)
pix2pixHD - Synthesizing and manipulating 2048x1024 images with conditional GANs
datatap-python - Focus on Algorithm Design, Not on Data Wrangling
CycleGAN - Software that can generate photos from paintings, turn horses into zebras, perform style transfer, and more.
PyTorchZeroToAll - Simple PyTorch Tutorials Zero to ALL!
pytorch-AdaIN - Unofficial pytorch implementation of 'Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization' [Huang+, ICCV2017]
ludwig - Low-code framework for building custom LLMs, neural networks, and other AI models
vrn - :man: Code for "Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression"
horovod - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. [Moved to: https://github.com/horovod/horovod]
mmagic - OpenMMLab Multimodal Advanced, Generative, and Intelligent Creation Toolbox. Unlock the magic 🪄: Generative-AI (AIGC), easy-to-use APIs, awsome model zoo, diffusion models, for text-to-image generation, image/video restoration/enhancement, etc.
PyTorch-GAN - PyTorch implementations of Generative Adversarial Networks.
pytorch-CycleGAN-and-pix2pix - Image-to-Image Translation in PyTorch