The paper tackles ambiguous image-to-image translation by modeling a distribution of possible outputs.
problem Ambiguity in image-to-image translation where a single input can have multiple possible outputs.
method The approach involves a conditional generative model that learns to map input images to a latent vector, which is then used to generate diverse outputs.
result The method produces more diverse and realistic outputs compared to other variants.
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.
ExGANs use exemplar information to improve eye in-painting results.
problem Improving photo-realistic eye in-painting results.
method Conditional GAN with exemplar information at multiple points.
result ExGANs produce photo-realistic, personalized in-painting results.
Paper proposes using NLPD for cGANs to improve image realism and segmentation.
problem Lack of perceptual evaluation in cGANs.
method Used NLPD to measure perceptual similarity, comparing with L1 distance.
result NLPD improves realism and segmentation accuracy in generated images.
Generative model improves sound synthesis realism.
problem Synthesize natural sounds with realistic perception.
method Latent force modelling with physical knowledge incorporated.
result Generated sounds perceived as more realistic.
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.
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.
New SSIM measure improves adversarial attacks' perceptual quality.
problem Lack of perceptual quality in traditional adversarial attacks.
method Replaced Lp norms with SSIM measure for better quality. result SSIM-constrained attacks achieve better quality and similar success rate.
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…
Perceptual Kalman filters maintain human-perceptual quality while processing data.
problem Maintaining human-perceptual quality in signal processing under temporal constraints.
method An optimal causal filtering approach under a perfect perceptual-quality constraint.
result Adding perceptual quality constraints introduces a dilemma that requires sacrificing MSE for temporal consistency.
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.
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.
New finding: Perceptually-aligned gradients occur in adversarially-robust classifiers.
problem Understanding adversarial robustness in neural networks.
method Investigated perceptual alignment in adversarially-trained and smoothed classifiers.
result Perceptually-aligned gradients are a general property of robust classifiers.
New method defends against unseen threat models using perceptual adversarial training.
problem Lack of precise mathematical characterization of human perception in adversarial attacks.
method Adversarial training against the set of all imperceptible adversarial examples approximated by deep neural networks.
result Perceptual Adversarial Training (PAT) achieves state-of-the-art robustness against multiple diverse adversarial attacks.
Perceptual ad-blocking is vulnerable to attacks, creating new security risks.
problem Vulnerability of perceptual ad-blocking to attacks and new security risks.
method Analysis and creation of adversarial examples to bypass perceptual ad-blocking.
result Perceptual ad-blocking can be bypassed using adversarial examples, introducing new security risks.
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.
Synthetic social networks closely match real-world interactions.
problem Evaluating realism of synthetic social contact networks.
method Used multiple measures of graph complexity to compare synthetic networks with stylized models and empirical data.
result Synthetic networks are more realistic than stylized models.
Study shows perceptual boost of visual attention varies with task difficulty and size.
problem Understanding how task-dependent the perceptual boost of visual attention is in natural settings.
method Designed and trained neural networks on various visual tasks, comparing results to baseline.
result Perceptual boost of attention is stronger with more difficult tasks and weaker with larger task sets.
This work introduces a new threat model for adversarial attacks using perceptual metrics.
problem Insufficient threat models for capturing imperceivable adversarial examples.
method Leverage quantitative perceptual metrics (LPIPS, SSIM) to define a novel threat model.
result Combined attacks retain perceptual distortion but induce higher misclassification rates.
PerceptNet learns haptic signal similarity using human data.
problem Designing haptic icons requires accurate perceptual similarity estimation.
method Deep neural network projecting signals to an embedding space with a triplet loss.
result Our method effectively models perceptual dissimilarity compared to alternatives.
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.
New framework learns phoneme metric from perception data.
problem Learn metric for phoneme similarity from data.
method Learning algorithms to derive metric from behavioral data.
result Framework outperforms previous metrics in phoneme prediction.
Introduces PCG for better counterfactual explanations in vision models.
problem Ambiguity in latent-space optimization methods for counterfactual explanations.
method Constructs counterfactuals by tracing geodesics under a perceptually Riemannian metric.
result PCG outperforms baselines and reveals hidden failure modes.
New model separates object attributes for better perceptual grouping.
problem Perceptual grouping of complex visual scenes.
method Spatial mixture models with learnable priors.
result Outperforms state-of-the-art methods in perceptual grouping.
Paper uses CNN to create realistic LiDAR data.
problem Creating high-resolution LiDAR data with missing points.
method Modified per-point loss and perceptual loss applied to generate realistic LiDAR data.
result Significant improvement in geometric and semantic accuracy over traditional methods.
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.
A new perceptual adjustment query for metric learning reduces complexity in high-dimensional data.
problem Metric learning in high-dimensional data with limited human feedback.
method Inverted measurement scheme and two-stage estimator for PAQs.
result Sample complexity guarantees for the two-stage estimator of metric learning from PAQs.
New study shows tradeoffs between compression quality, distortion, and perception.
problem Optimizing compression for low distortion often sacrifices perceptual quality.
method Adopted Blau & Michaeli's perceptual quality definition and studied the rate-distortion-perception tradeoff.
result Restricting perceptual quality to high generally requires a trade-off between rate and distortion.
PerceptNet mimics human vision to estimate image quality.
problem Estimating perceptual distance between images and their perturbations.
method Inspired by human visual system, PerceptNet uses convolutional neural network architecture.
result PerceptNet outperforms traditional image quality metrics and deep learning methods.
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.
Stochastic encoders outperform deterministic ones in 'perfect perceptual quality'.
problem Understanding when stochastic encoders outperform deterministic ones.
method Provided a toy example to illustrate performance.
result Stochastic encoders can significantly outperform deterministic ones in 'perfect perceptual quality'.
Conventional SVM-based image coding methods are founded on independently restricting the distortion in every image coefficient at some particular image representation. Geometrically, this implies allowing arbitrary signal distortions in an n-dimensional rectangle defined by the ε-insensitivity zone in eac…
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.
Neural network VQ-VAE with WaveNet decodes speech at 1.6 kbps with high quality.
problem Efficiently transmitting and storing speech signals at low bit-rates.
method VQ-VAE and WaveNet architecture for speech coding.
result Speech coding at 1.6 kbps with perceptual quality between MELP and AMR-WB.
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.
PeL separates sensory interface optimization from decision learning.
problem Optimizing sensory interfaces without task-specific information.
method Formal separation of perception and decision learning, using metrics for stability, informativeness, and geometry.
result Updates preserving invariants are orthogonal to decision gradients.
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.
Improved image generation with private data using perceptual features.
problem Difficulty in training generative models with differential privacy.
method Use pre-trained perceptual features to learn private data distribution.
result Generative models can generate high-quality images with low privacy budget (ϵ≈2). New method disentangles perceptual uncertainty and behavioral costs in partially observable systems.
problem Tackles inverse optimal control for non-linear partially observable systems.
method Probabilistic approach using maximum causal entropy formulations and local linearization.
result Disentangles perceptual factors and behavioral costs in sequential decision-making.
Spatially transformed adversarial examples are perceptually realistic and harder to defend against.
problem Vulnerability of deep neural networks to adversarial examples.
method Spatial transformation of images to generate adversarial examples.
result Spatially transformed adversarial examples are more difficult to defend against existing methods.
Stabilizes training of DNN for speech enhancement using PESQ scores.
problem Stability issues in training DNNs using non-differentiable OSQA scores.
method Approximate OSQA scores with a differentiable auxiliary DNN and stabilize training with reinforcement learning techniques.
result Stable training of DNN to achieve state-of-the-art PESQ scores and better sound quality.
Study neural systems' classification of perceptual manifolds.
problem Classifying neural responses to diverse sensory features.
method Statistical mechanics theory for linear classification of manifolds.
result Introduction of novel geometrical measures for manifold analysis.
PCI combines perception and control using Bayesian inference with object-based representations.
problem Separate perception and control in reinforcement learning.
method Joint Perception and Control as Inference (PCI) framework with Object-based Perception Control (OPC).
result OPC achieves good perceptual grouping quality and outperforms baselines in accumulated rewards.
Paper introduces adversarial lossy compression for video artifacts reduction.
problem Unpleasant reconstruction artifacts in standard video coding schemes at low bit-rates.
method Adversarial lossy video compression model minimizing an adversarial distortion objective.
result Reduction of perceptual artifacts and detail reconstruction under extreme compression.
A new loss function for VAEs improves image quality and efficiency.
problem Training VAEs to generate realistic images requires a loss function that reflects human perception.
method Based on Watson's perceptual model, the loss function computes a weighted distance in frequency space, accounts for luminance and contrast masking, and is extended to color images.
result VAEs trained with the new loss function generated high-quality, less blurry images with fewer artifacts and less computational resources.
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.
Paper presents a new video generation model using diffusion probabilistic methods.
problem Generating high-quality video sequences.
method Denoising diffusion probabilistic models, autoregressive, end-to-end optimization.
result Significant improvements in perceptual quality and probabilistic frame forecasting.
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.