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Title: Identifying Analogies Across Domains

Paper content:
Identifying analogies across domains without supervision is a key task for artificial intelligence.
Recent advances in cross domain image mapping have concentrated on translating images across domains.
Although the progress made is impressive, the visual fidelity many times does not suffice for identifying the matching sample from the other domain.
In this paper, we tackle this very task of finding exact analogies between datasets i.e. for every image from domain A find an analogous image in domain B. We present a matching-by-synthesis approach: AN-GAN, and show that it outperforms current techniques.
We further show that the cross-domain mapping task can be broken into two parts: domain alignment and learning the mapping function.
The tasks can be iteratively solved, and as the alignment is improved, the unsupervised translation function reaches quality comparable to full supervision.
Humans are remarkable in their ability to enter an unseen domain and make analogies to the previously seen domain without prior supervision ("This dinosaur looks just like my dog Fluffy").
This ability is important for using previous knowledge in order to obtain strong priors on the new situation, which makes identifying analogies between multiple domains an important problem for Artificial Intelligence.
Much of the recent success of AI has been in supervised problems, i.e., when explicit correspondences between the input and output were specified on a training set.
Analogy identification is different in that no explicit example analogies are given in advance, as the new domain is unseen.Recently several approaches were proposed for unsupervised mapping between domains.
The approaches take as input sets of images from two different domains A and B without explicit correspondences between the images in each set, e.g. Domain A: a set of aerial photos and Domain B: a set of Google-Maps images.
The methods learn a mapping function T AB that takes an image in one domain and maps it to its likely appearance in the other domain, e.g. map an aerial photo to a Google-Maps image.
This is achieved by utilizing two constraints: (i) Distributional constraint: the distributions of mapped A domain images (T AB (x)) and images of the target domain B must be indistinguishable, and (ii) Cycle constraint: an image mapped to the other domain and back must be unchanged, i.e., T BA (T AB (x)) = x.In this paper the task of analogy identification refers to finding pairs of examples in the two domains that are related by a fixed non-linear transformation.
Although the two above constraints have been found effective for training a mapping function that is able to translate between the domains, the translated images are often not of high enough visual fidelity to be able to perform exact matching.
We hypothesize that it is caused due to not having exemplar-based constraints but rather constraints on the distributions and the inversion property.In this work we tackle the problem of analogy identification.
We find that although current methods are not designed for this task, it is possible to add exemplar-based constraints in order to recover high performance in visual analogy identification.
We show that our method is effective also when only some of the sample images in A and B have exact analogies whereas the rest do not have exact analogies in the sample sets.
We also show that it is able to find correspondences between sets when no exact correspondences are available at all.
In the latter case, since the method retrieves rather than maps examples, it naturally yields far better visual quality than the mapping function.
Using the domain alignment described above, it is now possible to perform a two step approach for training a domain mapping function, which is more accurate than the results provided by previous unsupervised mapping approaches:1.
Find the analogies between the A and B domain, using our method.2.
Once the domains are aligned, fit a translation function T AB between the domains y mi = T AB (x i ) using a fully supervised method.
For the supervised network, larger architectures and non-adversarial loss functions can be used.
We presented an algorithm for performing cross domain matching in an unsupervised way.
Previous work focused on mapping between images across domains, often resulting in mapped images that were too inaccurate to find their exact matches.
In this work we introduced the exemplar constraint, specifically designed to improve match performance.
Our method was evaluated on several public datasets for full and partial exact matching and has significantly outperformed baseline methods.
It has been shown to work well even in cases where exact matches are not available.
This paper presents an alternative view of domain translation.
Instead of performing the full operation end-toend it is possible to
(i) align the domains, and
(ii) train a fully supervised mapping function between the aligned domains.Future work is needed to explore matching between different modalities such as images, speech and text.
As current distribution matching algorithms are insufficient for this challenging scenario, new ones would need to be developed in order to achieve this goal.

assistant: text: Finding correspondences between domains by performing matching/mapping iterations