Vanilla GANs are connected to Wasserstein distance for better understanding.
problem Understanding the statistical properties of Vanilla GANs.
method Connecting Vanilla GANs to Wasserstein distance and proving an oracle inequality.
result An oracle inequality for Vanilla GANs in Wasserstein distance is obtained.
This paper compares two GAN architectures to reduce mode collapse.
problem Reducing mode collapse in Generative Adversarial Networks (GANs).
method Explains and compares PacGAN and VEEGAN models.
result PacGAN performs slightly better than vanilla GAN in terms of mode collapse.
This work investigates image augmentations for GAN training, improving image quality.
problem Improving the accuracy and robustness of GAN models for image synthesis.
method Systematic study of various image augmentation techniques for GAN training.
result Vanilla GANs can achieve state-of-the-art generation quality with image augmentations.
A study on α-GANs proving convergence and estimation guarantees.
problem Analyzing the convergence and estimation guarantees of α-GANs. method Proved a correspondence between α-GANs and f-divergences, and provided estimation bounds. result Estimation bounds indicate diverse GAN behavior as a function of α. Survey of GANs and autoencoders, addressing mode collapse and likelihood issues.
problem Addressing mode collapse and likelihood issues in GANs and autoencoders.
method Explains various GAN and autoencoder variants, their applications, and methods to resolve issues.
result Various methods to resolve mode collapse and improve likelihood in GANs and autoencoders.
Improved GANs estimate convergence rate for density estimation.
problem Improving the accuracy of density estimation with GANs.
method Proved an oracle inequality for JS divergence between GAN estimate and true density.
result JS-divergence rate of convergence is (logn/n)2β/(2β+d). TorchGAN simplifies GAN training and evaluation with a flexible framework.
problem Training and evaluating GANs can be complex and time-consuming.
method Modular design, extensibility, built-in support for popular models and metrics.
result TorchGAN achieves near-zero overhead compared to vanilla PyTorch.
Improves GAN-based semi-supervised learning with consistency regularization.
problem Lack of consistency in class probability predictions under local perturbations.
method Introduces consistency regularization to GANs, leveraging both local and interpolation consistency.
result Significantly improves performance and achieves new state-of-the-art results.
Generative adversarial network (GAN) is a minimax game between a generator mimicking the true model and a discriminator distinguishing the samples produced by the generator from the real training samples. Given an unconstrained discriminator able to approximate any function, this game reduces to finding the generative …
We developed a new class of physics-informed generative adversarial networks (PI-GANs) to solve in a unified manner forward, inverse and mixed stochastic problems based on a limited number of scattered measurements. Unlike standard GANs relying only on data for training, here we encoded into the architecture of GANs th…
The paper introduces a new Wasserstein distance for approximating posteriors in inverse problems.
problem Approximating posterior measures in inverse problems using conditional Wasserstein distances.
method Introduces a conditional Wasserstein distance with restricted couplings and derives its dual.
result Shows that conditional Wasserstein GANs can yield favorable properties for posterior sampling.
Kernels are powerful and versatile tools in machine learning and statistics. Although the notion of universal kernels and characteristic kernels has been studied, kernel selection still greatly influences the empirical performance. While learning the kernel in a data driven way has been investigated, in this paper we e…
Owing to their connection with generative adversarial networks (GANs), saddle-point problems have recently attracted considerable interest in machine learning and beyond. By necessity, most theoretical guarantees revolve around convex-concave (or even linear) problems; however, making theoretical inroads towards effici…
This paper predicts stock prices using BERT for sentiment analysis and GAN for technical indicators.
problem Predicting stock prices using advanced techniques.
method BERT for sentiment analysis, GAN for technical indicators, comparison with baseline models.
result GAN outperformed baseline models in predicting stock prices.
New algorithms improve convergence of minimax optimization.
problem Minimax optimization convergence issues in nonconvex problems.
method Established new convergence results for two single-loop algorithms.
result Improved convergence rates for minimax optimization.
Improved LIME robustness against adversarial manipulation.
problem LIME's naive sampling strategy can be exploited to hide biased behavior.
method Training a GAN to generate more realistic synthetic data for explanations.
result Our method increases accuracy in detecting biased behavior compared to vanilla LIME.
Optimal hedging strategies for exotic options using vanilla options.
problem Hedging exotic options with illiquid vanilla options.
method Simple approximations and variational techniques in a market model and stochastic volatility model framework.
result Optimal Delta and Vega hedging strategies can be computed easily.
We present a generative autoencoder that provides fast encoding, faithful reconstructions (eg. retaining the identity of a face), sharp generated/reconstructed samples in high resolutions, and a well-structured latent space that supports semantic manipulation of the inputs. There are no current autoencoder or GAN model…
FELICIA uses a centralized adversary to improve synthetic medical image generation.
problem Collaborative learning with limited and biased data in medical image analysis.
method Federated generative modeling with a centralized adversary.
result Data owners can generate high-quality synthetic images with high utility without sharing real data.
Paper uses AI for more efficient hedging of financial options.
problem Inefficient hedging in financial models.
method RL agents and GANs for delta hedging.
result RL-based hedging outperforms classic models in Q-world.
This paper uses basket option formulas to price vanilla options with discrete dividends.
problem Pricing vanilla options on stocks with discrete cash dividends.
method Uses existing basket option formulas for European options on a single asset with cash dividends in the piecewise lognormal model.
result Explains the use of basket option formulas for a specific problem in the piecewise lognormal model.
DDSME outperforms SME in estimating multimodal distributions.
problem Efficiency of score matching in multimodal distributions.
method Diffusion-based denoising score matching (DDSME) compared to vanilla score matching (SME).
result DDSME avoids the error bound deterioration of SME with increasing mode separation.
A new GAN model α-GAN with tunable loss function addresses gradient vanishing and mode collapse issues.
problem Addressing vanishing gradients and mode collapse in GANs.
method Introduced a tunable GAN α-GAN using a supervised α-loss function. result Holistic understanding of α-GAN related to Arimoto divergence and convergence properties. Unbalanced GANs stabilize GAN training by pre-training the generator with VAE.
problem Stable training of GANs to avoid mode collapses and improve image quality.
method Pre-train GAN generator with VAE, balance generator and discriminator training, prevent discriminator's early convergence.
result Unbalanced GANs reduce mode collapses and outperform ordinary GANs in stability, convergence, and image quality.
GANs can approximate SDEs for large time steps.
problem Approximating SDEs for large time steps using GANs.
method Proposed a conditional GAN architecture to enable strong approximation of SDEs.
result Supervised GAN outperformed standard GAN and other schemes in strong error.
Deep vanilla transformers trained without shortcuts achieve similar performance to standard models.
problem Training deep vanilla transformers without shortcuts and normalizations.
method Parameter initializations, bias matrices, and location-dependent rescaling.
result Deep vanilla transformers can train at similar speeds and performance to standard models.
Understanding proper distance measures between distributions is at the core of several learning tasks such as generative models, domain adaptation, clustering, etc. In this work, we focus on mixture distributions that arise naturally in several application domains where the data contains different sub-populations. For …
Vanilla Bayesian optimization performs well in high dimensions.
problem Bayesian optimization's poor performance in high-dimensional problems.
method Identified and addressed degeneracies, proposed scaling of Gaussian process lengthscale prior.
result Vanilla Bayesian optimization outperforms existing algorithms in high-dimensional tasks.
Paper bridges f-GANs and WGANs for better image generation.
problem Learning high-dimensional distributions using GANs.
method List constraints, minimize Lagrangian relaxation, propose KL-Wasserstein GAN.
result Empirical success on synthetic and real-world image generation benchmarks.
New method uses neural networks for better financial hedging.
problem Spanning multi-asset payoffs with vanilla options.
method One-hidden-layer feedforward neural networks for numerical solution.
result Better hedging results with neural networks compared to single-asset approaches.
Vanilla SGD learns SIM from anisotropic data without explicit covariance estimation.
problem Learning SIM from anisotropic Gaussian inputs.
method Vanilla Stochastic Gradient Descent (SGD) trained on SIM with anisotropic input.
result Vanilla SGD adapts to anisotropic data's covariance structure.
Prb-GAN uses dropout and variational inference to improve GAN performance.
problem GANs struggle with mode loss and training instability.
method Introduces Prb-GANs with dropout and variational inference for parameter distribution.
result Improves GAN performance through dropout and variational inference.
xAI-GAN improves GANs by providing richer feedback, enhancing image quality.
problem Challenges in GAN training, especially resource-intensive and data-intensive.
method Integrates xAI systems to provide richer corrective feedback from discriminators to generators.
result Improves image quality by up to 23.18% on MNIST and FMNIST datasets.
This paper reviews GANs algorithms, theory, and applications.
problem Lack of comprehensive study on GANs connections and evolution.
method Detailed introduction of GANs algorithms, theoretical investigation, and application illustrations.
result Comprehensive review of GANs from algorithms, theory, and applications perspectives.
Generative Adversarial Networks (GANs) are powerful models for learning complex distributions. Stable training of GANs has been addressed in many recent works which explore different metrics between distributions. In this paper we introduce Fisher GAN which fits within the Integral Probability Metrics (IPM) framework f…
A recent technical breakthrough in the domain of machine learning is the discovery and the multiple applications of Generative Adversarial Networks (GANs). Those generative models are computationally demanding, as a GAN is composed of two deep neural networks, and because it trains on large datasets. A GAN is generally…
We investigate the training and performance of generative adversarial networks using the Maximum Mean Discrepancy (MMD) as critic, termed MMD GANs. As our main theoretical contribution, we clarify the situation with bias in GAN loss functions raised by recent work: we show that gradient estimators used in the optimizat…
GANs may not have Nash equilibria, but proximal training can find solutions.
problem Existence of Nash equilibria in GANs optimization.
method Proximal training approach to find solutions.
result Proximal training finds solutions to GAN problems.
Empirical study shows GANs overfit and drop modes when training is deterministic.
problem Understanding overfitting and mode drop in GAN training.
method Empirical analysis of GAN training with and without stochasticity.
result GANs overfit and drop modes when training is deterministic.
GANs generate images of emotions from datasets.
problem Creating realistic images of emotions from datasets.
method Used GANs and StarGAN to train on datasets of emotions.
result StarGAN successfully generated images of 7 basic emotions.
Generative Adversarial Networks (GANs) have been used in several machine learning tasks such as domain transfer, super resolution, and synthetic data generation. State-of-the-art GANs often use tens of millions of parameters, making them expensive to deploy for applications in low SWAP (size, weight, and power) hardwar…
GANs analyzed for performance and training issues.
problem Performance and training issues of GANs.
method SDE approximations for training GANs.
result Improved understanding of GANs through analytical perspectives.
Despite excellent progress in recent years, mode collapse remains a major unsolved problem in generative adversarial networks (GANs).In this paper, we present spectral regularization for GANs (SR-GANs), a new and robust method for combating the mode collapse problem in GANs. Theoretical analysis shows that the optimal …
Several dihedral angles prediction methods were developed for protein structure prediction and their other applications. However, distribution of predicted angles would not be similar to that of real angles. To address this we employed generative adversarial networks (GAN). Generative adversarial networks are composed …
Generative adversarial networks (GAN) have been effective for learning generative models for real-world data. However, existing GANs (GAN and its variants) tend to suffer from training problems such as instability and mode collapse. In this paper, we propose a novel GAN framework called evolutionary generative adversar…
Unified framework compresses GANs up to 47x with minimal quality loss.
problem High parameter complexity of GANs for resource-constrained devices.
method Unified optimization framework combining model distillation, channel pruning, and quantization.
result 47x compression of CartoonGAN with minimal quality degradation.
NR-GANs learn clean images from noisy data.
problem Learning clean images from noisy training data.
method Introduced a noise generator and distribution/transformation constraints.
result NR-GANs can generate clean images from noisy data.
New method improves GANs by estimating density ratios in feature space with SP loss.
problem Filtering out unrealistic images from GANs trained with suboptimal discriminators.
method Develops DRE-F-SP method based on Softplus loss for density ratio estimation in feature space, and proposes three subsampling methods.
result Empirically shows substantial improvement over existing methods on synthetic and CIFAR-10 datasets.