Inthispaper,weproposeareciprocative learning algorithm to exploit visual attention for training deep classifiers.

Synthetic visual data can provide practically infinite diversity and rich labels, while avoiding ethical issues with privacy and bias.

Frequently,transformations occurring in data can be better represented by a subset of a group than by agroup asawhole, e.g., rotations in[ 90,90 ]. Insuch cases, amodel that respects equivariancepartially is better suited to represent the data.

However, within this specific application domain, existing VAE methods are restricted by using only one layer of latent variables andstrictly Gaussian posterior approximations.

Toour knowledge, such guarantees in this setting are providedforthefirsttime.

We demonstrate the abilities of the proposed paradigm for image deblurring, super-resolution, and compressive sensing.

This perspectiveparallels an earlier phenomenon inthe much better understood field of optimization where convexity has played a preponderant role for both theoretical and methodological advances [Nes04; Bub15].

There are mainly two types of domain adaptation formulas:covariate shift[44, 37, 29, 13] and label shift [27, 2, 1], while we focus on the former in this paper since it appears more natural in recognition tasks and attracts more attention in the literature.

We propose contrastive coding to learn shared, dense image representations, referredtoasCoMIRs (ContrastiveMultimodal ImageRepresentations).

To this end, a fairly common distributed learning framework,namely dataparallelism,distributesthe(huge)data-setsovermultiple workermachines to exploit the power of parallel computing.