Papers Perceptual Distance
“Perceptual Distance” 태그가 달린 논문 33편 · 필터 해제
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases
Olfaction -- how molecules are perceived as odors to humans -- remains poorly understood. Recently, the principal odor map (POM) was introduced to digitize the olfactory properties of single compounds. However, smells in…
Perceptual DistanceRateless Stochastic Coding for Delay-Constrained Semantic Communication
We consider the problem of joint source-channel coding for semantic communication from a rateless perspective, the purpose of which is to settle the balance between reliability (distortion/perception) and effectiveness (…
DecoderPerceptual DistanceQuantizationSemantic CommunicationWords Worth a Thousand Pictures: Measuring and Understanding Perceptual Variability in Text-to-Image Generation
Diffusion models are the state of the art in text-to-image generation, but their perceptual variability remains understudied. In this paper, we examine how prompts affect image variability in black-box diffusion-based mo…
Image GenerationPerceptual DistanceText to Image GenerationText-to-Image GenerationEvaluating Perceptual Distance Models by Fitting Binomial Distributions to Two-Alternative Forced Choice Data
The two-alternative forced choice (2AFC) experimental method is popular in the visual perception literature, where practitioners aim to understand how human observers perceive distances within triplets made of a referenc…
Decision MakingPerceptual DistanceTripletADS: Approximate Densest Subgraph for Novel Image Discovery
The volume of image repositories continues to grow. Despite the availability of content-based addressing, we still lack a lightweight tool that allows us to discover images of distinct characteristics from a large collec…
Perceptual DistanceExploring Compressed Image Representation as a Perceptual Proxy: A Study
We propose an end-to-end learned image compression codec wherein the analysis transform is jointly trained with an object classification task. This study affirms that the compressed latent representation can predict huma…
Image CompressionPerceptual DistanceProjection Regret: Reducing Background Bias for Novelty Detection via Diffusion Models
Novelty detection is a fundamental task of machine learning which aims to detect abnormal ($\textit{i.e.}$ out-of-distribution (OOD)) samples. Since diffusion models have recently emerged as the de facto standard generat…
Novelty DetectionPerceptual DistanceHWD: A Novel Evaluation Score for Styled Handwritten Text Generation
Styled Handwritten Text Generation (Styled HTG) is an important task in document analysis, aiming to generate text images with the handwriting of given reference images. In recent years, there has been significant progre…
Image GenerationPerceptual DistanceText GenerationAdversarial Image Generation by Spatial Transformation in Perceptual Colorspaces
Deep neural networks are known to be vulnerable to adversarial perturbations. The amount of these perturbations are generally quantified using $L_p$ metrics, such as $L_0$, $L_2$ and $L_\infty$. However, even when the me…
Image GenerationPerceptual DistanceCPIPS: Learning to Preserve Perceptual Distances in End-to-End Image Compression
Lossy image coding standards such as JPEG and MPEG have successfully achieved high compression rates for human consumption of multimedia data. However, with the increasing prevalence of IoT devices, drones, and self-driv…
Image CompressionPerceptual DistanceSelf-Driving CarsHow Will It Drape Like? Capturing Fabric Mechanics from Depth Images
We propose a method to estimate the mechanical parameters of fabrics using a casual capture setup with a depth camera. Our approach enables to create mechanically-correct digital representations of real-world textile mat…
Data AugmentationMaterial RecognitionPerceptual DistanceTransfer LearningTowards Better Robustness against Common Corruptions for Unsupervised Domain Adaptation
Recent studies have investigated how to achieve robustness for unsupervised domain adaptation (UDA). While most efforts focus on adversarial robustness, i.e. how the model performs against unseen malicious adversaria…
Adversarial RobustnessData AugmentationDomain AdaptationPerceptual Distance+1Improving Perceptual Quality of Adversarial Images Using Perceptual Distance Minimization and Normalized Variance Weighting
Neural networks are known to be vulnerable to adversarial examples, which are obtained by adding intentionally crafted perturbations to original images. However, these perturbations degrade their perceptual quality and m…
Perceptual DistancePalette: Image-to-Image Diffusion Models
This paper develops a unified framework for image-to-image translation based on conditional diffusion models and evaluates this framework on four challenging image-to-image translation tasks, namely colorization, inpaint…
ColorizationDenoisingDiversityImage-to-Image Translation+4Semantic and Geometric Unfolding of StyleGAN Latent Space
Generative adversarial networks (GANs) have proven to be surprisingly efficient for image editing by inverting and manipulating the latent code corresponding to a natural image. This property emerges from the disentangle…
AttributeDisentanglementPerceptual DistanceUnderstanding and Simplifying Perceptual Distances
Perceptual metrics based on features of deep Convolutional Neural Networks (CNNs) have shown remarkable success when used as loss functions in a range of computer vision problems and significantly outperform classica…
Perceptual DistanceOn the relation between statistical learning and perceptual distances
It has been demonstrated many times that the behavior of the human visual system is connected to the statistics of natural images. Since machine learning relies on the statistics of training data as well, the above conne…
BIG-bench Machine LearningPerceptual DistanceRelationWhere and What? Examining Interpretable Disentangled Representations
Capturing interpretable variations has long been one of the goals in disentanglement learning. However, unlike the independence assumption, interpretability has rarely been exploited to encourage disentanglement in the u…
DisentanglementModel SelectionPerceptual DistanceShape-driven Coordinate Ordering for Star Glyph Sets via Reinforcement Learning
We present a neural optimization model trained with reinforcement learning to solve the coordinate ordering problem for sets of star glyphs. Given a set of star glyphs associated to multiple class labels, we propose to u…
DecoderPerceptual Distancereinforcement-learningReinforcement Learning (RL)Perceptual Adversarial Robustness: Generalizable Defenses Against Unforeseen Threat Models
A key challenge in adversarial robustness is the lack of a precise mathematical characterization of human perception, used in the definition of adversarial attacks that are imperceptible to human eyes. Most current attac…
Adversarial DefenseAdversarial RobustnessPerceptual Distance