PyTorch-VAE
qubo-nn
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| PyTorch-VAE | qubo-nn | |
|---|---|---|
| 5 | 3 | |
| 5,989 | 40 | |
| - | - | |
| 0.0 | 0.0 | |
| 7 months ago | over 2 years ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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PyTorch-VAE
- Help with VAE
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Confusions on VAE implementation
I am a beginner in VAE implementation and I am currently going through codes here.
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Confusion regarding Variational AutoEncoder Implementation
I am referring to the code in the link here for VAE code. I have the following questions and confusions:
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How to extract feature from 2 tensors into one? what layer should be used?
Here's a repo that has a large number of VAE variants for Pytorch: https://github.com/AntixK/PyTorch-VAE
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VAEs
Face embedding with VAEs https://github.com/AntixK/PyTorch-VAE
qubo-nn
- Reverse-Engineering QUBO Matrices
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How do you handle imbalanced datasets?
Here is an example.
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I accidentally tried to train 1 Quadrillion parameters - and a story of structural bias
In the course of that project (see here: https://github.com/instance01/qubo-nn) the second part turned out to be quite tough, likely due to the high dimensionality (4096 input size). In the post above I go through a mistake I made in which I assumed underfitting whereas in reality my data had structural bias (randomness that cannot be learnt by a neural network).
What are some alternatives?
Awesome-VAEs - A curated list of awesome work on VAEs, disentanglement, representation learning, and generative models.
Cirq - A python framework for creating, editing, and invoking Noisy Intermediate Scale Quantum (NISQ) circuits.
Robo-Semantic-Segmentation - Just a simple semantic segmentation library that I developed to speed up the image segmentation pipeline
quantum-nearest-classifier - Cirq implementation of Nearest Centroid Classification on a Trapped Ion Quantum Computer (Johri et al., 2020)
6DRepNet - Official Pytorch implementation of 6DRepNet: 6D Rotation representation for unconstrained head pose estimation.
qsearch - A compiler for quantum computers based on A* and numerical optimization.
disentangling-vae - Experiments for understanding disentanglement in VAE latent representations
torch-metrics - Metrics for model evaluation in pytorch
benchmark_VAE - Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)
Amortized-SVGD-GAN - Learning to draw samples: with application to amortized maximum likelihood estimator for generative adversarial learning
Advanced-Deep-Learning-with-Keras - Advanced Deep Learning with Keras, published by Packt
minimal_VAE_on_Mario - A minimal VAE trained on Super Mario Bros levels.