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]: Image-to-image translation has recently received significant attention due to advances in deep learning. Most works focus on learning either a one-to-one mapping in an unsupervised way or a many-to-many mapping in a supervised way. However, a more practical setting is many-to-many mapping in an unsupervised way, which is harder due to the lack of supervision and the complex inner- and cross-domain variations. To alleviate these issues, we propose the Exemplar Guided & Semantically Consistent Image-to-image Translation (EGSC-IT) network which conditions the translation process on an exemplar image in the target domain. We assume that an image comprises of a content component which is shared across domains, and a style component specific to each domain. Under the guidance of an exemplar from the target domain we apply Adaptive Instance Normalization to the shared content component, which allows us to transfer the style information of the target domain to the source domain. To avoid semantic inconsistencies during translation that naturally appear due to the large inner- and cross-domain variations, we introduce the concept of feature masks that provide coarse semantic guidance without requiring the use of any semantic labels. Experimental results on various datasets show that EGSC-IT does not only translate the source image to diverse instances in the target domain, but also preserves the semantic consistency during the process.
[EX A]: We propose the Exemplar Guided & Semantically Consistent Image-to-image Translation (EGSC-IT) network which conditions the translation process on an exemplar image in the target domain.

[EX Q]: Deep generative models can emulate the perceptual properties of complex image datasets, providing a latent representation of the data. However, manipulating such representation to perform meaningful and controllable transformations in the data space remains challenging without some form of supervision. While previous work has focused on exploiting statistical independence to \textit{disentangle} latent factors, we argue that such requirement can be advantageously relaxed and propose instead a non-statistical framework that relies on identifying a modular organization of the network, based on counterfactual manipulations. Our experiments support that modularity between groups of channels is achieved to a certain degree on a variety of generative models. This allowed the design of targeted interventions on complex image datasets, opening the way to applications such as computationally efficient style transfer and the automated assessment of robustness to contextual changes in pattern recognition systems.
[EX A]: We develop a framework to find modular internal representations in generative models and manipulate then to generate counterfactual examples.

[EX Q]: We explore the idea of compositional set embeddings that can be used to infer not
 just a single class, but the set of classes associated with the input data (e.g., image,
 video, audio signal). This can be useful, for example, in multi-object detection in
 images, or multi-speaker diarization (one-shot learning) in audio. In particular, we
 devise and implement two novel models consisting of (1) an embedding function
 f trained jointly with a “composite” function g that computes set union opera-
 tions between the classes encoded in two embedding vectors; and (2) embedding
 f trained jointly with a “query” function h that computes whether the classes en-
 coded in one embedding subsume the classes encoded in another embedding. In
 contrast to prior work, these models must both perceive the classes associated
 with the input examples, and also encode the relationships between different class
 label sets. In experiments conducted on simulated data, OmniGlot, and COCO
 datasets, the proposed composite embedding models outperform baselines based
 on traditional embedding approaches.
[EX A]:
We explored how a novel method of compositional set embeddings can both perceive and represent not just a single class but an entire set of classes that is associated with the input data.