In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

[EX Q]: Domain transfer is a exciting and challenging branch of machine learning because models must learn to smoothly transfer between domains, preserving local variations and capturing many aspects of variation without labels.
However, most successful applications to date require the two domains to be closely related (ex. image-to-image, video-video),
utilizing similar or shared networks to transform domain specific properties like texture, coloring, and line shapes.
Here, we demonstrate that it is possible to transfer across modalities (ex. image-to-audio) by first abstracting the data with latent generative models and then learning transformations between latent spaces.
We find that a simple variational autoencoder is able to learn a shared latent space to bridge between two generative models in an unsupervised fashion, and even between different types of models (ex. variational autoencoder and a generative adversarial network).
We can further impose desired semantic alignment of attributes with a linear classifier in the shared latent space.
The proposed variation autoencoder enables preserving both locality and semantic alignment through the transfer process, as shown in the qualitative and quantitative evaluations.
Finally, the hierarchical structure decouples the cost of training the base generative models and semantic alignments, enabling computationally efficient and data efficient retraining of personalized mapping functions.
[EX A]: Conditional VAE on top of latent spaces of pre-trained generative models that enables transfer between drastically different domains while preserving locality and semantic alignment.

[EX Q]: We present Cross-View Training (CVT), a simple but effective method for deep semi-supervised learning. On labeled examples, the model is trained with standard cross-entropy loss. On an unlabeled example, the model first performs inference (acting as a "teacher") to produce soft targets. The model then learns from these soft targets (acting as a ''"student"). We deviate from prior work by adding multiple auxiliary student prediction layers to the model. The input to each student layer is a sub-network of the full model that has a restricted view of the input (e.g., only seeing one region of an image). The students can learn from the teacher (the full model) because the teacher sees more of each example. Concurrently, the students improve the quality of the representations used by the teacher as they learn to make predictions with limited data. When combined with Virtual Adversarial Training, CVT improves upon the current state-of-the-art on semi-supervised CIFAR-10 and semi-supervised SVHN. We also apply CVT to train models on five natural language processing tasks using hundreds of millions of sentences of unlabeled data. On all tasks CVT substantially outperforms supervised learning alone, resulting in models that improve upon or are competitive with the current state-of-the-art.

[EX A]: Self-training with different views of the input gives excellent results for semi-supervised image recognition, sequence tagging, and dependency parsing.

[EX Q]: In this paper, we propose a novel approach to improve a given surface mapping through local refinement. The approach
receives an established mapping between two surfaces and follows four phases: (i) inspection of the mapping and creation of a sparse
set of landmarks in mismatching regions; (ii) segmentation with a low-distortion region-growing process based on flattening the
segmented parts; (iii) optimization of the deformation of segmented parts to align the landmarks in the planar parameterization domain;
and (iv) aggregation of the mappings from segments to update the surface mapping. In addition, we propose a new method to deform the
mesh in order to meet constraints (in our case, the landmark alignment of phase (iii)). We incrementally adjust the cotangent weights for
the constraints and apply the deformation in a fashion that guarantees that the deformed mesh will be free of flipped faces and will have
low conformal distortion. Our new deformation approach, Iterative Least Squares Conformal Mapping (ILSCM), outperforms other
low-distortion deformation methods. The approach is general, and we tested it by improving the mappings from different existing surface
mapping methods. We also tested its effectiveness by editing the mappings for a variety of 3D objects.
[EX A]:
We propose a novel approach to improve a given cross-surface mapping through local refinement with a new iterative method to deform the mesh in order to meet user constraints.