Image Domain Adaptation Papers 17/19

A subset of nice domain adaptation papers with a short depiction. No warranty for correctness! Within an Unsupervised Domain Adaptation Setting (UDA), annotations are available only in the source domain and images in both domains. Presented papers mainly introduce appproaches to solve this issue in the image domain.

Image-to-Image Translation

Domain Transformation in Image Space

Abbr.	Paper Title	Publ.	Link	Comment
DTN	Unsupervised Cross Domain Image Generation	Nov 2016	link	Generates an avatar to a corresponding celeb face. Shared feature space. Cycle consistency. Only one direction. Similiar to UNIT.
SimGAN	Learning from Simulated and Unsupervised Images through AdversarialTraining	Dec 2016	link	GAN is a Refiner network (keeps semantic), starting to use img instead of noise as input, using self regularization pixel distance to keep semantic and use sim labels
	Unsupervised Image-to-Image Translation with Generative Adversarial Networks	Jan 2017	link	Face transformation. Two-step learning method to translate images between different domains. No closed training in one step. Multi-domain ability. 1st generator learns to generate fake samples, 2nd encoder learns to reconstruct noise input of generator
UNIT	Unsupervised Image-to-Image Translation Networks	Mar 2017	link	Two coupled VAE-GANs. Fully shared latent space. Cycle consistency. Adapts textures. Unimodal: gaussian latent space. Works for small imgs. Translates both directions.
CycleGAN	Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks	Mar 2017	link	Builds on the pix2pix framework. Big images but can loose semantics. No VAE. Difference to pix2pix: translation in both directions (cycle loss)
cdGAN	Conditional Image-to-Image Translation	May 2018	link	Two gans: dual gan. Learns separated domain-independent and domain-specific features. No style sampling possible but different target images are possible with different style targets.
DCAN	Dual Channel-wise Alignment Networks for Unsupervised Scene Adaptation	Apr 2018	link	Channel-wise feature alignment in the generator and segmentation networks. Can extend other image translation models.
	Learning image-to-image translation using paired and unpaired training samples	2018	link	Using paired and unpaired data during training in a two-stage process. Overall 4 Discriminators
---------	In combination with classification
PixelDA	Unsupervised Pixel–Level Domain Adaptationwith Generative Adversarial Networks	Dec 2016	link	Decoupling from the Task-Specific Architecture. No latent space, no encoder.
	Generate to Adapt	Apr 2017	link	GAN: target domain is translated into source domain (source domain determines feature space!). Classfication task on embedding.
---------	In combination with segmentation
I2IAdapt	Image to Image Translation for Domain Adaptation	Dec 2017	link	Similar to UNIT/CycleGAN. Two GANs, cycle consistency, fully shared latent space, segmentation task on latent space.
	Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation	Nov 2017	link	Simultaneous training of image generation (from feature map) and segmentation in latent space. One GAN, fully shared latent space.
CyCADA	Cycle Consistent Adversarial Domain Adaptation	Nov 2017	link	Segm. on image space. Cycle consistency, adversarial learning on images and features. Semantic consistency loss on stylized source image.
SPIGAN	Privileged Adversarial Learning from Simulation	Oct 2018	link	Using depth maps from synthia as Priviledged Information during training to promote segmentation. Like DADA.
BDL	Bidirectional Learning for Domain Adaptation of Semantic Segmentation	2019	link	Simultaneous training of image translation and segmentation with Selftraining. Feedback from task to image tranlsation process.
---------	Multimodal
BicycleGAN	Toward Multimodal Image-to-Image Translation	Nov 2017	link	Explicitly modeled multimodal domain-specific latent space. Style sampling possible. Conditional VAE-GAN combined with a latent regressor loss.
StarGAN	Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation	Nov 2017	link	Simultaneous training on different datasets. Synthesizing facial expressions of celebrities, while using features learned from a dataset with different face expressions.
MUNIT	Multimodal Unsupervised Image-to-Image Translation	Apr 2018	link	Based strongly on UNIT. Partially shared latent space assumption instead of fully shared latent space (UNIT). Here, only the content space is shared. Every domain has own style space. Sampling of styles possible.

Semantic Image Synthesis

Synhtesizing realistic images from a semantic layout.

Abbr.	Paper Title	Publication	Link	Comment
pix2pix	Image-to-Image Translation with Conditional Adversarial Networks	nov 2016	link	Real street scenery from segmentation mask. cGAN. no VAE. Trained on paired data!
pix2pixHD	High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs	2018	link	Trained on paired data as well. Photo-realism through enhancer networks. Using border maps with layouts as extended input, a feature matching loss, and multi-scale discriminators.
SPADE	Semantic Image Synthesis with Spatially-Adaptive Normalization	2019	link	Random vector as input instead of a semantic layout. The semantic segmentation is used to modulate the activations in normalization layers.
	Learning to Predict Layout-to-image Conditional Convolutions for Semantic Image Synthesis	2019	link	A weight prediction network, with the layout as input condition, predicts the kernel weights of generator network. The generator gets noise as input and synthesizes the images. The discriminator conisders whether the images look real or fake and their semantic alignment.
	Semantic Bottleneck Scene Generation	2019	link	They not only synthesize images, but also the semantic layouts. Using SPADE for image synthesis.

Latent Space Transformation

Domain adaptation in feature space

Abbr.	Paper Title	Publication	Link	Comment
---------	In combination with classification
	Unsupervised Domain Adaptation by Backpropagation	2015	link	First approach to keep features domain-agnostic.
ADDA	Adversarial Discriminative Domain Adaptation	2017	link	General 2-step approach to obtain equaling features for different domains (source domain determines feature space!)
	Adversarial Feature Augmentation for Unsupervised Domain Adaptation	2018	link	Adversarial training in feature space (source domain determines feature space!)
	Unsupervised Domain Adaptation with Similarity Learning	2018	link	Based on similarity to prototypes(source domain determines feature space!)
CADA	Attending to Discriminative Certainty for Domain Adaptation	2019	link	No entire-image-based domain adaptation, but to focus on regions that can be adapted better like foreground objects. Considers the probabilistic certainty estimate of various regions.
---------	In combination with segmentation
	Conditional Generative Adversarial Network for Structured Domain Adaptation	2018	link	Conditional generator translates feature maps, extracted from source images, into feature maps that are close to those of tagret images. No assumption of shared feature space: learning the residual between the feature maps from both domains
	Domain adaptation for semantic segmentation via class-balanced self-traingin	2018		Introducing self-training for segmentation model.
	Learning to Adapt Structured Output Space for Semantic Segmentation	2018	link	Adversarial learning on segmentation output (gap between domains is smaller there). Including multi-level domain adaptation in shared encoder.
DADA	Depth-Aware Domain Adaptation in Semantic Segmentation	2019	link	Unified depth-aware UDA framework using depth map as priviledged information in order to boost the segmentation task. Like SPIGAN.
	Domain Adaptation for Structured Output via Discriminative Patch Representations	2019	link	Samples patches from source images and performs k-means clustering on patches. Aim is to get strcutured output by assigning patches to clusters