The paper discusses quantifying realism in generated images.
problem Designing functions to reliably distinguish realistic data from unrealistic data.
method Drawing on insights from algorithmic information theory, the paper introduces the notion of a universal critic.
result A good generative model alone is insufficient to solve the problem of realism quantification.
New insights into image compression trade-offs with private randomness.
problem Trade-off between compression rate and perceptual quality in image compression.
method Characterization of rate-distortion trade-off with private randomness under different realism constraints.
result Encoder private randomness is not useful if compression rate is below source entropy, even with limited common and decoder private randomness.
The paper evaluates and improves uncertainty estimates in neural networks for safety-critical applications.
problem Quantifying uncertainty in neural networks for safety-critical systems.
method Proposes a statistical test for evaluating uncertainty realism in neural networks and transfers a classification architecture to image-to-image tasks.
result The variational U-Net architecture significantly improves uncertainty realism in image-to-image tasks compared to a plain model.
Paper proposes evaluation methods for climate change illustrations.
problem Lack of metrics to compare realism in conditional generative models.
method Adapted and assessed several existing metrics, including FID.
result FID with Inception-V3 embeddings correlates best with human realism.
Agents learn to draw with human-like abstraction and realism.
problem Training generative models to produce realistic images without supervision.
method Reinforcement learning agents in a simulated painting environment, trained with a discriminator network.
result Generative agents can produce images with visual abstraction and realism.
New method evaluates text-to-image synthesis for realism, variety, and semantic accuracy.
problem Lack of metrics revealing semantic accuracy in text-to-image synthesis.
method Uses Inception network representations and t-SNE visualization for semantic evaluation.
result Classification accuracy of generated images to real images' visual concepts correlates with semantic accuracy.
Single-image super-resolution (SISR) is a canonical problem with diverse applications. Leading methods like SRGAN produce images that contain various artifacts, such as high-frequency noise, hallucinated colours and shape distortions, which adversely affect the realism of the result. In this paper, we propose an altern…
Generative Adversarial Networks create realistic brain MRI images.
problem Lack of sufficient training data for medical research.
method Experimented three GAN architectures with different loss functions and hyperparameter tuning.
result Achieved high-quality, realistic synthetic MRIs with gradient penalty and mini-batch similarity layer.
DeDUCE efficiently generates realistic counterfactuals for image classifiers.
problem Generating accurate counterfactual explanations for large image classifiers.
method Developed a new algorithm using spectral normalization to efficiently generate counterfactuals.
result Our algorithm consistently produces counterfactuals closer to the original inputs with comparable realism.
Proposes adversarial normalization for multi-domain image segmentation.
problem Current image normalization is per-dataset, limiting multi-domain segmentation.
method Adversarial training to learn common normalizing functions across multiple datasets.
result Optimal normalizer improves segmentation accuracy and realism.
The preceding three decades have seen the emergence, rise, and proliferation of machine learning (ML). From half-recognised beginnings in perceptrons, neural nets, and decision trees, algorithms that extract correlations (that is, patterns) from a set of data points have broken free from their origin in computational c…
Generative Adversarial Networks (GANs) can produce images of remarkable complexity and realism but are generally structured to sample from a single latent source ignoring the explicit spatial interaction between multiple entities that could be present in a scene. Capturing such complex interactions between different ob…
Generative Adversarial Networks (GANs) and their extensions have carved open many exciting ways to tackle well known and challenging medical image analysis problems such as medical image de-noising, reconstruction, segmentation, data simulation, detection or classification. Furthermore, their ability to synthesize imag…
Generative model generates images with multiple object classes.
problem Generating images with multiple object classes.
method Conditional Deep Convolutional GAN architecture, stabilized against collapse.
result System generates diverse samples with inter-object relationships.
Many image-to-image translation problems are ambiguous, as a single input image may correspond to multiple possible outputs. In this work, we aim to model a \emph{distribution} of possible outputs in a conditional generative modeling setting. The ambiguity of the mapping is distilled in a low-dimensional latent vector,…
Automatically evaluates image quality based on human judgment.
problem Difficulty in rigorously evaluating generated image quality.
method Generative model embeddings, human labels regression, and statistical matching.
result 66% accuracy in predicting human scores of image realism.
Pseudo rehearsal uses non-photo-realistic images to save resources without sacrificing performance.
problem Catastrophic forgetting in neural networks when learning new tasks.
method Synthetically generate non-photo-realistic images to rehearse previous tasks.
result Non-photo-realistic images can be used for rehearsal without sacrificing performance and significantly reduce resource consumption.
Develops a new model for controllable and realistic traffic simulation.
problem Lack of models that offer both controllability and realism in traffic simulation.
method Guided Conditional Diffusion (CTG) model using diffusion modeling and differentiable logic.
result Improves controllability-realism tradeoff over strong baselines.
DeepInversion generates images from trained networks without additional data.
problem Generating images from trained networks without real data.
method Inverting a trained network to synthesize images from random noise, optimizing using batch normalization layers and maximizing Jensen-Shannon divergence.
result Synthesized images from CIFAR-10 and ImageNet datasets show high fidelity and realism.
Study flaws in generative model evaluation metrics, especially for diffusion models.
problem Flaws in existing metrics for evaluating generative models, particularly for diffusion models.
method Systematic study of generative models, human perception experiments, and analysis of feature extractors.
result State-of-the-art perceptual realism of diffusion models is not reflected in commonly reported metrics.
InvGAN combines generative and inference models for photo-realistic image manipulation.
problem GANs lack an inference model for image editing and downstream tasks.
method Train inference and generative models together to adapt and converge.
result InvGAN embeds real images into a high-quality generative model's latent space.
Framework detects brain tumors robustly from MRI images.
problem Low clinical incidence of brain tumor cases makes diagnosis challenging.
method YOLOv8n for detection, DeiT for classification, PTP metric for evaluation.
result F1-score of 0.92 achieved with reduced computational resources.
Method generates anatomically-controllable medical images with segmentation guidance.
problem Challenging to enforce anatomical constraints in generated medical images.
method Segmentation-guided diffusion models with random mask ablation training.
result New state-of-the-art in faithfulness to input anatomical masks.
Diffusion-GAN uses diffusion to improve GAN training stability and realism.
problem Stability and realism issues in training GANs.
method Diffusion-GAN employs a forward diffusion chain to generate Gaussian-mixture distributed instance noise, with adaptive diffusion process and timestep-dependent discriminator.
result Diffusion-GAN produces more realistic images with higher stability and data efficiency.
State of the art deep generative networks are capable of producing images with such incredible realism that they can be suspected of memorizing training images. It is why it is not uncommon to include visualizations of training set nearest neighbors, to suggest generated images are not simply memorized. We demonstrate …
A new metric FRD improves comparing medical images.
problem Comparing medical images for distribution or domain differences.
method Developed a new metric FRD using standardized radiomic features.
result FRD outperforms other metrics in various medical imaging applications.
LLM-based trading systems vary in execution realism and reproducibility.
problem LLM-based trading systems vary in execution realism and reproducibility.
method Reproducibility audit of 30 trade-relevant primary studies.
result LLM-based trading systems vary in execution realism and reproducibility.
Humans can only interact with part of the surrounding environment due to biological restrictions. Therefore, we learn to reason the spatial relationships across a series of observations to piece together the surrounding environment. Inspired by such behavior and the fact that machines also have computational constraint…
Paper tackles object detection in limited data scenarios.
problem Limited annotated data for object detection.
method Generative modeling with a novel unrolling mechanism to optimize both generation and detection.
result Improves object detection performance on NIH Chest X-ray dataset by 20%.
New coding theorem shows achievable rate matches theoretical limit.
problem Unknown existence of encoders and decoders for RDPF.
method Used stochastic, variable-length codes to prove RDPF achievable.
result Achievable rate matches theoretical rate-distortion-perception function.
We use multiple measures of graph complexity to evaluate the realism of synthetically-generated networks of human activity, in comparison with several stylized network models as well as a collection of empirical networks from the literature. The synthetic networks are generated by integrating data about human populatio…
CounteRGAN generates realistic, actionable counterfactuals for machine learning models.
problem Creating realistic and actionable counterfactuals for machine learning models.
method Applying Residual GANs to improve counterfactual realism and actionability.
result CounteRGAN produces counterfactuals with improved realism and actionability, achieving real-time applicability.
Deep generative neural networks have proven effective at both conditional and unconditional modeling of complex data distributions. Conditional generation enables interactive control, but creating new controls often requires expensive retraining. In this paper, we develop a method to condition generation without retrai…
Generative adversarial approach for satellite image time series land cover classification.
problem Enhance interpretability of land cover classification models.
method Generative adversarial counterfactual approach for multi-class land cover classification.
result Discovery of interesting information on land cover class relationships and sparser, interpretable solutions.
Examines optimal risk sharing with realistic risk attitudes, finding risk seeking in certain subdomains.
problem Optimal risk sharing with empirically realistic risk attitudes.
method Allows for risk-seeking agents, generalizes expected utility, and uses counter-monotonic improvement theorem.
result First empirical results on optimal risk sharing with realistic risk attitudes.
SMAPGAN generates styled map tiles from remote sensing images.
problem Generating timely updated map tiles from remote sensing images is challenging.
method Semi-supervised GAN model with gradient loss and ESSI metric.
result SMAPGAN outperforms state-of-the-art methods in quality metrics and human perception.
Extends driving model to control agent behavior in simulations.
problem Simulate realistic driving behavior for autonomous systems.
method Introduces Control-ITRA method to influence agent behavior through waypoint assignment and target speed modulation.
result Demonstrates controllable, infraction-free trajectories while preserving realism.
This paper uses CGANs to simulate and improve trading agent performance in limit order books.
problem Improving trading agent performance in limit order book environments.
method Investigates conditional generative models (CGANs) for order book simulation and adversarial attacks to enhance realism and robustness.
result CGANs can be improved to better simulate real market conditions and are more robust to adversarial attacks.
Study assesses market simulation metrics to highlight discrepancies between real and simulated markets.
problem Lack of fidelity in market simulation methods leads to discrepancies between real and simulated market data.
method Surveyed and applied a set of reference metrics to real and simulated market data.
result Significant discrepancies remain between real and simulated markets.
Quantum theory challenges traditional cause-effect relations, showing causal influences even without Bell inequality violations.
problem Challenging traditional concepts of cause-effect relations in quantum mechanics.
method Introducing a general framework to estimate causal influences without interventions or classical/quantum assumptions.
result Every pure bipartite entangled state violates classical bounds on causal influence, negating the idea that Bell inequalities are the only signature of incompatibility.
Data assimilation for subsurface flow using latent diffusion models shows that ensemble Kalman methods may overestimate posterior uncertainty, while Monte Carlo sampling is more reliable.
problem Data assimilation for subsurface flow
method Ensemble Kalman smoother and Markov chain Monte Carlo sampling
result Monte Carlo sampling is more reliable than ensemble Kalman methods
DiffVolume generates realistic volume snapshots for LOBs.
problem Generating high-dimensional volume snapshots in LOBs is challenging.
method Conditional Diffusion model for volume generation.
result DiffVolume outperforms in realism, counterfactual generation, and downstream prediction.
This study investigates the effects of Markov chain Monte Carlo (MCMC) sampling in unsupervised Maximum Likelihood (ML) learning. Our attention is restricted to the family of unnormalized probability densities for which the negative log density (or energy function) is a ConvNet. We find that many of the techniques used…
Cost-effective method improves and re-purposes pre-trained GANs by fine-tuning class-embeddings.
problem Fine-tuning BigGANs from scratch is impractical due to instability and high computational cost.
method Fine-tuning only the class-embedding layer of pre-trained GANs.
result Significantly improved realism and diversity of samples, re-purposed for new tasks, and de-biased or improved diversity.
SFAG generates realistic financial data that passes trading tests.
problem Financial generative models often produce unrealistic and unstable trading outcomes.
method Introduces SFAG, a GAN variant that aligns stylized facts and optimizes with adversarial loss.
result SFAG generates synthetic data that preserves stylized facts and supports robust trading strategies.
Graph Beta Diffusion (GBD) generates graphs with mixed discrete and continuous components.
problem Generating graphs with mixed discrete and continuous components.
method Introduces Graph Beta Diffusion (GBD) using a beta diffusion process.
result Competes strongly with existing models across graph benchmarks.
SocialInteractionGAN generates realistic human interactions from low-dimensional data.
problem Generating realistic human interactions from limited data.
method Adversarial architecture with a recurrent encoder-decoder generator and dual-stream discriminator.
result SocialInteractionGAN produces high-quality action sequences of interacting people.
Generative Adversarial Networks simulate realistic market interactions.
problem Lack of agent-level historical data limits market simulation realism.
method Conditional Generative Adversarial Networks (CGANs) trained on real data.
result CGAN-based synthetic market generator outperforms previous methods in market responsiveness and realism.