gan-vae-pretrained-pytorch
pytorch-image-classification
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| gan-vae-pretrained-pytorch | pytorch-image-classification | |
|---|---|---|
| 1 | 1 | |
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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.
pytorch-image-classification
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neural networks project
https://github.com/bentrevett/pytorch-image-classification You can find plethora of options like the one above that I have sent, just google it, After mnist usually people go for image classification for differentiating between cats and dogs
What are some alternatives?
AvatarGAN - Generate Cartoon Images using Generative Adversarial Network
SimCLR - PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations
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!
PyTorch-Crash-Course - This is the repository for the PyTorch machine learning crash course.
AnimeGAN - Generating Anime Images by Implementing Deep Convolutional Generative Adversarial Networks paper
Speech-Emotion-Classification-by-utilizing-a-Convolutional-Neural-Network - Enhancing Speech Emotion Classification with CNNs: This project seeks to overcome the limitations of traditional approaches and improve the accuracy of emotion recognition. CNNs automatically extract features from speech signals, capturing complex patterns and nuances, leading to enhanced performance compared to traditional methods.
AI-For-Beginners - 12 Weeks, 24 Lessons, AI for All!
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.
JoJoGAN - Official PyTorch repo for JoJoGAN: One Shot Face Stylization
dnn_from_scratch - A high level deep learning library for Convolutional Neural Networks,GANs and more, made from scratch(numpy/cupy implementation).
RefinementGAN - Official implementation of the paper: https://arxiv.org/abs/2108.04957
GFPGAN-for-Video-SR - A colab notebook for video super resolution using GFPGAN