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Found 19 papers, 8 papers with code
Fast Ensembling with Diffusion Schrödinger Bridge
Deep Ensemble (DE) approach is a straightforward technique used to enhance the performance of deep neural networks by training them from different initial points, converging towards various local optima.
Sequential Data Generation with Groupwise Diffusion Process
We present the Groupwise Diffusion Model (GDM), which divides data into multiple groups and diffuses one group at one time interval in the forward diffusion process.
Probabilistic Imputation for Time-series Classification with Missing Data
In this paper, we propose a novel probabilistic framework for classification with multivariate time series data with missing values.
User-friendly Image Editing with Minimal Text Input: Leveraging Captioning and Injection Techniques
From the users' standpoint, prompt engineering is a labor-intensive process, and users prefer to provide a target word for editing instead of a full sentence.
Regularizing Towards Soft Equivariance Under Mixed Symmetries
The key component of our method is what we call equivariance regularizer for a given type of symmetries, which measures how much a model is equivariant with respect to the symmetries of the type.
Context-Preserving Two-Stage Video Domain Translation for Portrait Stylization
Portrait stylization, which translates a real human face image into an artistically stylized image, has attracted considerable interest and many prior works have shown impressive quality in recent years.
Learning Input-agnostic Manipulation Directions in StyleGAN with Text Guidance
With the advantages of fast inference and human-friendly flexible manipulation, image-agnostic style manipulation via text guidance enables new applications that were not previously available.
BallGAN: 3D-aware Image Synthesis with a Spherical Background
To solve this problem, we propose to approximate the background as a spherical surface and represent a scene as a union of the foreground placed in the sphere and the thin spherical background.
Diffusion Video Autoencoders: Toward Temporally Consistent Face Video Editing via Disentangled Video Encoding
Inspired by the impressive performance of recent face image editing methods, several studies have been naturally proposed to extend these methods to the face video editing task.
Generator Knows What Discriminator Should Learn in Unconditional GANs
Here we explore the efficacy of dense supervision in unconditional generation and find generator feature maps can be an alternative of cost-expensive semantic label maps.
Memory Efficient Patch-based Training for INR-based GANs
However, training existing approaches require a heavy computational cost proportional to the image resolution, since they compute an MLP operation for every (x, y) coordinate.
Generating Videos with Dynamics-aware Implicit Generative Adversarial Networks
In this paper, we found that the recent emerging paradigm of implicit neural representations (INRs) that encodes a continuous signal into a parameterized neural network effectively mitigates the issue.
Ranked #25 on
Video Generation
on UCF-101
Exploiting Spatial Dimensions of Latent in GAN for Real-time Image Editing
Although manipulating the latent vectors controls the synthesized outputs, editing real images with GANs suffers from i) time-consuming optimization for projecting real images to the latent vectors, ii) or inaccurate embedding through an encoder.
Deep Hedging, Generative Adversarial Networks, and Beyond
This paper introduces a potential application of deep learning and artificial intelligence in finance, particularly its application in hedging.
A StyleMap-Based Generator for Real-Time Image Projection and Local Editing
State-of-the-art GAN-based methods for editing real images suffer from time-consuming operations in projecting real images to latent vectors.
Adaptive Strategy for Resetting a Non-stationary Markov Chain during Learning via Joint Stochastic Approximation
The non-stationary kernel problem refers to the degraded performance of the algorithm due to the constant change of the transition kernel of the chain throughout the run of the algorithm.
Tag2Pix: Line Art Colorization Using Text Tag With SECat and Changing Loss
A GAN approach is proposed, called Tag2Pix, of line art colorization which takes as input a grayscale line art and color tag information and produces a quality colored image.
Data Cleaning for Accurate, Fair, and Robust Models: A Big Data - AI Integration Approach
As machine learning is used in sensitive applications, it becomes imperative that the trained model is accurate, fair, and robust to attacks.
