GFPGAN-for-Video-SR
gan-vae-pretrained-pytorch
GFPGAN-for-Video-SR | gan-vae-pretrained-pytorch | |
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31 | 162 | |
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0.0 | 0.0 | |
11 months ago | over 2 years ago | |
Jupyter Notebook | Jupyter Notebook | |
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GFPGAN-for-Video-SR
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I Upscaled Linus's first video (Better results than Topaz Labs)
If you want to upscale your own videos , check out my colab notebook to do it for free : https://github.com/GeeveGeorge/GFPGAN-for-Video-SR
gan-vae-pretrained-pytorch
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DCGAN (CIFAR-10) Generating fake images is easy, but how to also output the class label (1 to 10) with the fake generated images?
I have this DCGAN model (https://github.com/csinva/gan-vae-pretrained-pytorch/tree/master/cifar10_dcgan) which generates fake Cifar-10 images. However I also want to get the intended class label output with the fake generated images. How can I do this? This model which I found only generates fake images but doesn't know what class the generated images belong to.
What are some alternatives?
nn - 🧑🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
AvatarGAN - Generate Cartoon Images using Generative Adversarial Network
Real-ESRGAN-Video-Batch-Process - Upscale any number of videos using this colab notebook!
pytorch-GAT - My implementation of the original GAT paper (Veličković et al.). I've additionally included the playground.py file for visualizing the Cora dataset, GAT embeddings, an attention mechanism, and entropy histograms. I've supported both Cora (transductive) and PPI (inductive) examples!
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AI-For-Beginners - 12 Weeks, 24 Lessons, AI for All!
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Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning - My Computer Vision project from my Computer Vision Course (Fall 2020) at Goethe University Frankfurt, Germany. Performance comparison between state-of-the-art Object Detection algorithms YOLO and Faster R-CNN based on the Berkeley DeepDrive (BDD100K) Dataset.
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