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

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Laplacian Score-regularized Concrete Autoencoders

Related tags

Overview

Laplacian Score-regularized Concrete Autoencoders

Requirements:

How to use:

Owner

JS

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Laplacian Score-regularized Concrete Autoencoders

Related tags

Overview

Laplacian Score-regularized Concrete Autoencoders

Requirements:

How to use:

Owner

JS

Reproduced Code for Image Forgery Detection papers.

PyTorch code for ICPR 2020 paper Future Urban Scene Generation Through Vehicle Synthesis

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The Most Efficient Temporal Difference Learning Framework for 2048

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Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentation, CVPR 2018

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The official PyTorch implementation of the paper: *Xili Dai, Xiaojun Yuan, Haigang Gong, Yi Ma. "Fully Convolutional Line Parsing." *.

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