1D CNN VAE on pytorch for MNIST Dataset

Hi, I hope you are doing fine. I have done many image processing and video processing projects in Matlab, Python, JAVA, etc. I already have worked with some well-known models like, YOLO, RCNN, Fast-RCNN, Faster-RCNN and etc. My PhD thesis was also visual analysis of human motion. I have also published several journal papers on the subject. You can see portfolio for my previous projects. If you are interested, Please contact with more information and we can discuss it more thoroughly. Thank you for taking time to go through my proposal and I hope to hear from you. Best regards.

Hello this is Collins and am a Machine Learning expert, I have gone through your project and I will be able to perform the following; • Write and comment the meaning of the input of a 1D CNN and others used in pytorch and use the MNIST dataset for it. • Plot the 2D latent space generated by training a 1D CNN VAE and ensure the latent space corresponds to that obtained for 1D CNN VAE of tensorflow (see attached). • The first notebook is done for tensorflow and you can use ideas of the network structure for tensorflow to design your pytorch version. PLease get in touch

I have extensively worked with CAE and VAEs and I am very efficient with PyTorch as well! Would love to help you out with the migration of code from Tensorflow to PyTorch with well commented code.

Hi, I'm student researcher in deep learning and have fair amount of experience in the workings of deep neural networks. I've worked with Vanilla-VAE, Conditional-VAE, and Beta-VAE, in the past. I've also worked on Bayesian Neural Networks as well which also uses variational inference, ELBO loss, kl-divergence, etc. I went through the notebooks attached. Currently your code doesn't work on cpu as it has grad_scaler (which is meant for gpu), also it doesn't work on gpu either (because the tensors are not at appropriate places). So, it seems that the code has quite some bugs. I'll be able to fulfil all your requirements. But regarding correspondence of latent space plot of 1D-CNN (pytorch) to 1D-CNN (tensorflow), you may expect different results with Vanilla-VAE and Beta-VAE. Tensorflow version is currently using Beta-VAE. So if you want Beta-VAE in PyTorch, that'll again take some time as it is currently not implemented in PyTorch. Therefore, I might need a couple of days more.