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Found 16 papers, 3 papers with code
Improved Positional Encoding for Implicit Neural Representation based Compact Data Representation
Positional encodings are employed to capture the high frequency information of the encoded signals in implicit neural representation (INR).
Latent-Shift: Gradient of Entropy Helps Neural Codecs
End-to-end image/video codecs are getting competitive compared to traditional compression techniques that have been developed through decades of manual engineering efforts.
RQAT-INR: Improved Implicit Neural Image Compression
Deep variational autoencoders for image and video compression have gained significant attraction in the recent years, due to their potential to offer competitive or better compression rates compared to the decades long traditional codecs such as AVC, HEVC or VVC.
Reducing The Mismatch Between Marginal and Learned Distributions in Neural Video Compression
In this paper, we propose to evaluate the amortization gap for three state-of-the-art ML video compression methods.
Video Coding Using Learned Latent GAN Compression
We leverage the generative capacity of GANs such as StyleGAN to represent and compress a video, including intra and inter compression.
FacialFilmroll: High-resolution multi-shot video editing
We present FacialFilmroll, a solution for spatially and temporally consistent editing of faces in one or multiple shots.
Learning Perceptual Compression of Facial Video
We leverage the generative capacity of GANs such as StyleGAN to represent and compress each video frame (intra compression), as well as the successive differences between frames (inter compression).
Semantic 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.
Wasserstein Adversarial Regularization (WAR) on label noise
Noisy labels often occur in vision datasets, especially when they are obtained from crowdsourcing or Web scraping.
An Entropic Optimal Transport Loss for Learning Deep Neural Networks under Label Noise in Remote Sensing Images
The state-of-the-art performances of deep neural networks are conditioned to the availability of large number of accurately labeled samples.
Randomized ICA and LDA Dimensionality Reduction Methods for Hyperspectral Image Classification
In literature a fewer number of pixels are randomly selected to partial to overcome this issue, however this sub-optimal strategy might neglect important information in the HSI.
Fast Optimal Bandwidth Selection for RBF Kernel using Reproducing Kernel Hilbert Space Operators for Kernel Based Classifiers
Thus, the objective of this letter is to propose an fast and efficient method to select the bandwidth parameter of the Gaussian kernel in the kernel based classification methods.
DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation
In computer vision, one is often confronted with problems of domain shifts, which occur when one applies a classifier trained on a source dataset to target data sharing similar characteristics (e. g. same classes), but also different latent data structures (e. g. different acquisition conditions).
Ranked #2 on
Domain Adaptation
on MNIST-to-MNIST-M
Large Scale Optimal Transport and Mapping Estimation
First, we learn an optimal transport (OT) plan, which can be thought as a one-to-many map between the two distributions.
Data Dependent Kernel Approximation using Pseudo Random Fourier Features
Thus, reducing the number of feature dimensions is necessary to effectively scale to large datasets.
Large-Scale Optimal Transport and Mapping Estimation
We prove two theoretical stability results of regularized OT which show that our estimations converge to the OT plan and Monge map between the underlying continuous measures.
