Full Resolution Image Compression with Recurrent Neural Networks

This paper presents a set of full-resolution lossy image compression methods based on neural networks. Each of the architectures we describe can provide variable compression rates during deployment without requiring retraining of the network: each network need only be trained once... (read more)

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Methods used in the Paper


METHOD TYPE
Sigmoid Activation
Activation Functions
Tanh Activation
Activation Functions
Adam
Stochastic Optimization
1x1 Convolution
Convolutions
Convolution
Convolutions
LSTM
Recurrent Neural Networks
Masked Convolution
Convolutions
PixelRNN
Generative Models
Residual GRU
Recurrent Neural Networks
Residual Connection
Skip Connections
GRU
Recurrent Neural Networks