233 citations · 557 across the 31 of their papers we have counts for
6 papers · 2 filters
Dataset Diffusion: Diffusion-based Synthetic Dataset Generation for Pixel-Level Semantic Segmentation
Quang Nguyen, Truong Vu, Anh Tran +1
Preparing training data for deep vision models is a labor-intensive task. To address this, generative models have emerged as an effective solution for generating synthetic data. Wh…
Flow Matching in Latent Space
Quan Dao, Hao Phung, Binh Nguyen +1
Flow matching is a recent framework to train generative models that exhibits impressive empirical performance while being relatively easier to train compared with diffusion-based m…
Efficient Scale-Invariant Generator with Column-Row Entangled Pixel Synthesis
Thuan Hoang Nguyen, Thanh Van Le, Anh Tran
Any-scale image synthesis offers an efficient and scalable solution to synthesize photo-realistic images at any scale, even going beyond 2K resolution. However, existing GAN-based…
HyperCUT: Video Sequence from a Single Blurry Image using Unsupervised Ordering
Bang-Dang Pham, Phong Tran, Anh Tran +3
We consider the challenging task of training models for image-to-video deblurring, which aims to recover a sequence of sharp images corresponding to a given blurry image input. A c…
Learning for Amalgamation: A Multi-Source Transfer Learning Framework For Sentiment Classification
Cuong V. Nguyen, Khiem H. Le, Anh M. Tran +2
Transfer learning plays an essential role in Deep Learning, which can remarkably improve the performance of the target domain, whose training data is not sufficient. Our work explo…
Anti-DreamBooth: Protecting users from personalized text-to-image synthesis
Thanh Van Le, Hao Phung, Thuan Hoang Nguyen +3
Text-to-image diffusion models are nothing but a revolution, allowing anyone, even without design skills, to create realistic images from simple text inputs. With powerful personal…