Laplacian Score-regularized Concrete Autoencoders

Last update: Dec 07, 2022

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Deep Learning lscae

Overview

Laplacian Score-regularized Concrete Autoencoders

Requirements:

torch >= 1.9
scikit-learn >= 0.24
omegaconf >= 2.0.6
scipy >= 1.6.0
matplotlib

How to use:

Install the package from pypi: pip install lscae

import lscae
import torch
from omegaconf import OmegaConf

# define you cfg parameters
cfg = OmegaConf.create({
    "input_dim": 100 })
# define you dataset (Torch based)
dataset = torch.utils.data.Dataset(...)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=cfg.batch_size, shuffle=True, drop_last=True)
lscae.Lscae(kwargs=cfg).select_features(dataloader)

Please see the full example here

Owner

JS

Applied Data Scientist

GitHub Repository

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Laplacian Score-regularized Concrete Autoencoders

Related tags

Overview

Laplacian Score-regularized Concrete Autoencoders

Requirements:

How to use:

Owner

JS

[AAAI2021] The source code for our paper 《Enhancing Unsupervised Video Representation Learning by Decoupling the Scene and the Motion》.

The code for paper Efficiently Solve the Max-cut Problem via a Quantum Qubit Rotation Algorithm

Use stochastic processes to generate samples and use them to train a fully-connected neural network based on Keras

Image Captioning using CNN ,LSTM and Attention

ViewFormer: NeRF-free Neural Rendering from Few Images Using Transformers

BigbrotherBENL - Face recognition on the Big Brother episodes in Belgium and the Netherlands.

Deep Learning GPU Training System

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Emblaze - Interactive Embedding Comparison

PyTorch implementation for 3D human pose estimation

Python scripts for performing object detection with the 1000 labels of the ImageNet dataset in ONNX.

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Train CPPNs as a Generative Model, using Generative Adversarial Networks and Variational Autoencoder techniques to produce high resolution images.

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