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Found 31 papers, 17 papers with code
Nickel and Diming Your GAN: A Dual-Method Approach to Enhancing GAN Efficiency via Knowledge Distillation
In this paper, we address the challenge of compressing generative adversarial networks (GANs) for deployment in resource-constrained environments by proposing two novel methodologies: Distribution Matching for Efficient compression (DiME) and Network Interactive Compression via Knowledge Exchange and Learning (NICKEL).
PosterLlama: Bridging Design Ability of Langauge Model to Contents-Aware Layout Generation
Visual layout plays a critical role in graphic design fields such as advertising, posters, and web UI design.
Hybrid Video Diffusion Models with 2D Triplane and 3D Wavelet Representation
Generating high-quality videos that synthesize desired realistic content is a challenging task due to their intricate high-dimensionality and complexity of videos.
STREAM: Spatio-TempoRal Evaluation and Analysis Metric for Video Generative Models
To the best of our knowledge, STREAM is the first evaluation metric that can separately assess the temporal and spatial aspects of videos.
Bridging the Domain Gap: A Simple Domain Matching Method for Reference-based Image Super-Resolution in Remote Sensing
To the best of our knowledge, we are the first to explore Domain Matching-based RefSR in remote sensing image processing.
RADIO: Reference-Agnostic Dubbing Video Synthesis
One of the most challenging problems in audio-driven talking head generation is achieving high-fidelity detail while ensuring precise synchronization.
TopP&R: Robust Support Estimation Approach for Evaluating Fidelity and Diversity in Generative Models
To the best of our knowledge, this is the first evaluation metric focused on the robust estimation of the support and provides its statistical consistency under noise.
Efficient Storage of Fine-Tuned Models via Low-Rank Approximation of Weight Residuals
In this paper, we present an efficient method for storing fine-tuned models by leveraging the low-rank properties of weight residuals.
Fix the Noise: Disentangling Source Feature for Controllable Domain Translation
This allows our method to smoothly control the degree to which it preserves source features while generating images from an entirely new domain using only a single model.
Can We Find Strong Lottery Tickets in Generative Models?
To the best of our knowledge, we are the first to show the existence of strong lottery tickets in generative models and provide an algorithm to find it stably.
LANIT: Language-Driven Image-to-Image Translation for Unlabeled Data
Existing techniques for image-to-image translation commonly have suffered from two critical problems: heavy reliance on per-sample domain annotation and/or inability of handling multiple attributes per image.
Rethinking the Truly Unsupervised Image-to-Image Translation
To this end, we propose a truly unsupervised image-to-image translation model (TUNIT) that simultaneously learns to separate image domains and translates input images into the estimated domains.
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results
This paper reviews the NTIRE 2020 challenge on real world super-resolution.
SimUSR: A Simple but Strong Baseline for Unsupervised Image Super-resolution
In this paper, we tackle a fully unsupervised super-resolution problem, i. e., neither paired images nor ground truth HR images.
Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategy
The key intuition of CutBlur is to enable a model to learn not only "how" but also "where" to super-resolve an image.
Reliable Fidelity and Diversity Metrics for Generative Models
In this paper, we show that even the latest version of the precision and recall metrics are not reliable yet.
StarGAN v2: Diverse Image Synthesis for Multiple Domains
A good image-to-image translation model should learn a mapping between different visual domains while satisfying the following properties: 1) diversity of generated images and 2) scalability over multiple domains.
Neural Approximation of an Auto-Regressive Process through Confidence Guided Sampling
We first assume that the priors of future samples can be generated in an independently and identically distributed (i. i. d.)
Time-Dependent Deep Image Prior for Dynamic MRI
The key ingredients of our method are threefold: 1) a fixed low-dimensional manifold that encodes the temporal variations of images; 2) a network that maps the manifold into a more expressive latent space; and 3) a convolutional neural network that generates a dynamic series of MRI images from the latent variables and that favors their consistency with the measurements in k-space.
ACE: Artificial Checkerboard Enhancer to Induce and Evade Adversarial Attacks
The checkerboard phenomenon is one of the well-known visual artifacts in the computer vision field.
Photorealistic Style Transfer via Wavelet Transforms
The key ingredient of our method is wavelet transforms that naturally fits in deep networks.
Large-Scale Answerer in Questioner's Mind for Visual Dialog Question Generation
Answerer in Questioner's Mind (AQM) is an information-theoretic framework that has been recently proposed for task-oriented dialog systems.
Multi-Domain Processing via Hybrid Denoising Networks for Speech Enhancement
We present a hybrid framework that leverages the trade-off between temporal and frequency precision in audio representations to improve the performance of speech enhancement task.
Audio and Speech Processing Sound
Deep Residual Learning for Accelerated MRI using Magnitude and Phase Networks
The proposed deep residual learning networks are composed of magnitude and phase networks that are separately trained.
A Mathematical Framework for Deep Learning in Elastic Source Imaging
An inverse elastic source problem with sparse measurements is of concern.
Deep Learning Diffuse Optical Tomography
Diffuse optical tomography (DOT) has been investigated as an alternative imaging modality for breast cancer detection thanks to its excellent contrast to hemoglobin oxidization level.
Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network
To address this problem, we recently proposed a deep convolutional neural network (CNN) for low-dose X-ray CT and won the second place in 2016 AAPM Low-Dose CT Grand Challenge.
Deep Learning with Domain Adaptation for Accelerated Projection-Reconstruction MR
To address the situation given the limited available data, we propose a domain adaptation scheme that employs a pre-trained network using a large number of x-ray computed tomography (CT) or synthesized radial MR datasets, which is then fine-tuned with only a few radial MR datasets.
Deep artifact learning for compressed sensing and parallel MRI
Furthermore, the computational time is by order of magnitude faster.
Beyond Deep Residual Learning for Image Restoration: Persistent Homology-Guided Manifold Simplification
To address this issue, here we propose a novel feature space deep residual learning algorithm that outperforms the existing residual learning.
Ranked #6 on
Color Image Denoising
on CBSD68 sigma50
Deep Residual Learning for Compressed Sensing CT Reconstruction via Persistent Homology Analysis
Recently, compressed sensing (CS) computed tomography (CT) using sparse projection views has been extensively investigated to reduce the potential risk of radiation to patient.
