DeepI2I: Enabling Deep Hierarchical Image-to-Image Translation by Transferring from GANs

Image-to-image translation has recently achieved remarkable results. But despite current success, it suffers from inferior performance when translations between classes require large shape changes... (read more)

PDF Abstract NeurIPS 2020 PDF NeurIPS 2020 Abstract

Datasets


Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper


METHOD TYPE
Softmax
Output Functions
Dense Connections
Feedforward Networks
Feedforward Network
Feedforward Networks
Convolution
Convolutions
Adam
Stochastic Optimization
Dot-Product Attention
Attention Mechanisms
Non-Local Operation
Image Feature Extractors
Projection Discriminator
Discriminators
Batch Normalization
Normalization
ReLU
Activation Functions
SAGAN Self-Attention Module
Attention Modules
Conditional Batch Normalization
Normalization
1x1 Convolution
Convolutions
Early Stopping
Regularization
Spectral Normalization
Normalization
Residual Block
Skip Connection Blocks
Truncation Trick
Latent Variable Sampling
SAGAN
Generative Adversarial Networks
Residual Connection
Skip Connections
GAN Hinge Loss
Loss Functions
Non-Local Block
Image Model Blocks
Linear Layer
Feedforward Networks
TTUR
Optimization
Off-Diagonal Orthogonal Regularization
Regularization
BigGAN
Generative Models