ALAE | Adversarial Latent Autoencoders | Machine Learning library
kandi X-RAY | ALAE Summary
kandi X-RAY | ALAE Summary
Adversarial Latent Autoencoders Stanislav Pidhorskyi, Donald Adjeroh, Gianfranco Doretto Abstract: Autoencoder networks are unsupervised approaches aiming at combining generative and representational properties by learning simultaneously an encoder-generator map. Although studied extensively, the issues of whether they have the same generative power of GANs, or learn disentangled representations, have not been fully addressed. We introduce an autoencoder that tackles these issues jointly, which we call Adversarial Latent Autoencoder (ALAE). It is a general architecture that can leverage recent improvements on GAN training procedures. We designed two autoencoders: one based on a MLP encoder, and another based on a StyleGAN generator, which we call StyleALAE. We verify the disentanglement properties of both architectures. We show that StyleALAE can not only generate 1024x1024 face images with comparable quality of StyleGAN, but at the same resolution can also produce face reconstructions and manipulations based on real images. This makes ALAE the first autoencoder able to compare with, and go beyond the capabilities of a generator-only type of architecture.
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Top functions reviewed by kandi - BETA
- Parse command line arguments
- Setup nccl
- Center crop of images
- Run torch
- Prepare the dataset to be used in the ensemble
- Train the model
- Tag the last checkpoint
- Loads the model from scratch
- Save a checkpoint to disk
- Sample from the model
- Downscale image
- Evaluate the model
- Encodes a tensor
- Evaluate principal directions
- Load a pickled file
- Parse a tf record
- Forward the convolution layer
- Scale a 2D tensor
- Forward convolutional layer
- Minibatch standard deviation layer
- Make a sequence
- Return True if arg is a sequence
- Align landmarks
- Perform the forward transformation
- Crop the image to center
- Increment the next iteration
- Encodes the given tensor
ALAE Key Features
ALAE Examples and Code Snippets
python3 train_MlpALAE.py
python3 train_StyleGan.py --dataset_name LFW
python3 train_StyleALAE.py --dataset_name FFHQ
Community Discussions
Trending Discussions on ALAE
QUESTION
With git log
command I can successfully get a Github Pull Request number.
Now, with the Pull Request number known, I would like to go ahead and query the message (a comment) posted with the PR (the PR message is displayed under the Conversation
tab). Here is an example of PR with a message posted:
https://github.com/podgorskiy/ALAE/pull/11
How to query the PR message (aka PR comment) from a command line (with curl
or git
or something else)?
ANSWER
Answered 2020-May-04 at 06:01To get the PR message using curl
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install ALAE
You can use ALAE like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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