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Found 7 papers, 5 papers with code
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
Generative Adversarial Networks (GANs) excel at creating realistic images with complex models for which maximum likelihood is infeasible.
Ranked #1 on
Image Generation
on LSUN Bedroom 64 x 64
Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields
We prove that Coulomb GANs possess only one Nash equilibrium which is optimal in the sense that the model distribution equals the target distribution.
A GAN based solver of black-box inverse problems
We propose a GAN based approach to solve inverse problems which have non-differential or non-continuous forward relations.
Modern Hopfield Networks and Attention for Immune Repertoire Classification
We show that the attention mechanism of transformer architectures is actually the update rule of modern Hopfield networks that can store exponentially many patterns.
Hopfield Networks is All You Need
The new update rule is equivalent to the attention mechanism used in transformers.
Immune Repertoire Classification
Multiple Instance Learning
+1
CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP
We suggest to use modern Hopfield networks to tackle the problem of explaining away.
Why current rain denoising models fail on CycleGAN created rain images in autonomous driving
In a second step, this paired good/bad weather image data is used to train two rain denoising models, one based primarily on a Convolutional Neural Network (CNN) and the other using a Vision Transformer.
