Papers › Energy-guided Entropic Neural Optimal Transport

Energy-guided Entropic Neural Optimal Transport

12 Apr 2023arXiv:2304.06094archive 2025-07-28

Petr Mokrov, Alexander Korotin, Alexander Kolesov, Nikita Gushchin, Evgeny Burnaev

Energy-based models (EBMs) are known in the Machine Learning community for decades. Since the seminal works devoted to EBMs dating back to the noughties, there have been a lot of efficient methods which solve the generative modelling problem by means of energy potentials (unnormalized likelihood functions). In contrast, the realm of Optimal Transport (OT) and, in particular, neural OT solvers is much less explored and limited by few recent works (excluding WGAN-based approaches which utilize OT as a loss function and do not model OT maps themselves). In our work, we bridge the gap between EBMs and Entropy-regularized OT. We present a novel methodology which allows utilizing the recent developments and technical improvements of the former in order to enrich the latter. From the theoretical perspective, we prove generalization bounds for our technique. In practice, we validate its applicability in toy 2D and image domains. To showcase the scalability, we empower our method with a pre-trained StyleGAN and apply it to high-res AFHQ 512×512 unpaired I2I translation. For simplicity, we choose simple short- and long-run EBMs as a backbone of our Energy-guided Entropic OT approach, leaving the application of more sophisticated EBMs for future research. Our code is available at: https://github.com/PetrMokrov/Energy-guided-Entropic-OT

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batch_jacobian petrmokrov/energy-guided-entropic-ot/src/utils.py official repository unverified MIT (permissive) · e6d9dfda0f695bd3 · report
clip_by_norm petrmokrov/energy-guided-entropic-ot/src/eot.py official repository unverified MIT (permissive) · b341005633913916 · report
conditional_sample_from_EgEOT petrmokrov/energy-guided-entropic-ot/src/eot_utils.py official repository unverified MIT (permissive) · eacdb4c52360ccae · report
l2sq_cost petrmokrov/energy-guided-entropic-ot/mnist2to3/eval_ot.py official repository unverified MIT (permissive) · b0bf915554f16952 · report
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sample_image_set petrmokrov/energy-guided-entropic-ot/mnist2to3/eval_ot.py official repository unverified MIT (permissive) · 9b5f33316d34439f · report
sample_langevin_batch petrmokrov/energy-guided-entropic-ot/src/eot.py official repository unverified MIT (permissive) · 5e8bf2ee50178f10 · report
sample_pseudo_langevin_batch petrmokrov/energy-guided-entropic-ot/src/eot.py official repository unverified MIT (permissive) · 3459932850a115b9 · report
spectral_norm petrmokrov/energy-guided-entropic-ot/src/models_utils.py official repository unverified MIT (permissive) · 1bded705b21cb40a · report
visualize_2d_contour petrmokrov/energy-guided-entropic-ot/dgm_utils/visualize.py official repository unverified MIT (permissive) · a48d15fe86871566 · report

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Generalization BoundsImage-to-Image Translation

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Adaptive Instance NormalizationConvolutionDense ConnectionsFeedforward NetworkR1 RegularizationStyleGANWGAN

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