autoencoder-architecture | Topic

Topic: "autoencoder-architecture"

ShrishtiHore/Anomaly-Detection-in-CCTV-Surveillance-Videos

This projects detect Anomalous Behavior through live CCTV camera feed to alert the police or local authority for faster response time. We are using Spatio Temporal AutoEncoder and more importantly three models from Keras ie; Convolutional 3D, Convolutional 2D LSTM and Convolutional 3D Transpose. 👮‍♂️👮‍♀️📹🔍🔫⚖

Language: Jupyter Notebook - Size: 46.7 MB - Last synced at: about 2 years ago - Pushed at: over 4 years ago - Stars: 16 - Forks: 4

DanuserLab/openLCH Fork of andrewjUTSW/openLCH

Live Cell Histology: Extracting latent features from label-free live cell images using Adversarial Autoencoders

Language: Lua - Size: 13.6 MB - Last synced at: about 2 years ago - Pushed at: over 4 years ago - Stars: 5 - Forks: 0

ayulockin/deepgenerativemodeling

Towards Generative Modeling from (variational) Autoencoder to DCGAN

Language: Jupyter Notebook - Size: 897 KB - Last synced at: 9 months ago - Pushed at: about 5 years ago - Stars: 3 - Forks: 1

rakibhhridoy/ImageDenoisingUsing-AutoEncoders

Filtering out the noise presented in the image by auto-enconder algorithm in TensorFow and Keras. Rare images, unclean crime images,medical noise images can be denoised and find out the desired outcome by using auto-encoders.

Language: Jupyter Notebook - Size: 2.18 MB - Last synced at: about 1 month ago - Pushed at: almost 5 years ago - Stars: 1 - Forks: 1

dame-cell/Masked-AutoEncoders

Pytorch implementation of Masked Autoencoder I

Language: Python - Size: 934 KB - Last synced at: 9 months ago - Pushed at: 9 months ago - Stars: 0 - Forks: 0

potatobox1/Person-Segmentation-with-Autoencoders

Person Segmentation using custom Autoencoder architecture and evaluation using IoU and Dice metrics, will also include Unet architecture in the future.

Language: Jupyter Notebook - Size: 11.6 MB - Last synced at: 10 months ago - Pushed at: 10 months ago - Stars: 0 - Forks: 0

HayatiYrtgl/autoencoder_colorization

Colorizes grayscale images using a loaded model and displays original and predicted colorized versions.

Language: Python - Size: 19.5 KB - Last synced at: 3 months ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

PrasunDatta/IEEE-Signal-Processing-Cup-2020_Unsupervied-Abnormality-Detection

In this research work, unsupervised abnormality has been detected by using intelligent and heterogeneous autonomous systems.

Language: Python - Size: 6.41 MB - Last synced at: about 1 year ago - Pushed at: over 2 years ago - Stars: 0 - Forks: 0

AdityaTheDev/ReconstructionOfImage-Using-DeepAutoEnccoders

Autoencoder is a type of neural network where the output layer has the same dimensionality as the input layer. In simpler words, the number of output units in the output layer is equal to the number of input units in the input layer. An autoencoder replicates the data from the input to the output in an unsupervised manner and is therefore sometimes referred to as a replicator neural network. The autoencoders reconstruct each dimension of the input by passing it through the network. It may seem trivial to use a neural network for the purpose of replicating the input, but during the replication process, the size of the input is reduced into its smaller representation. The middle layers of the neural network have a fewer number of units as compared to that of input or output layers. Therefore, the middle layers hold the reduced representation of the input. The output is reconstructed from this reduced representation of the input.

Language: Jupyter Notebook - Size: 706 KB - Last synced at: almost 2 years ago - Pushed at: over 3 years ago - Stars: 0 - Forks: 0

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