no code implementations • 4 Mar 2024 • Cong Geng, Tian Han, Peng-Tao Jiang, Hao Zhang, Jinwei Chen, Søren Hauberg, Bo Li
Generative models have shown strong generation ability while efficient likelihood estimation is less explored.
no code implementations • 13 Dec 2023 • Huizi Wu, Cong Geng, Hui Fang
Session-based recommendation (SR) aims to dynamically recommend items to a user based on a sequence of the most recent user-item interactions.
no code implementations • 26 Jan 2022 • Huizi Wu, Cong Geng, Hui Fang
Considering the varied interpretations and characteristics of causality and correlation relationship between items, in this study, we propose a novel method denoted as CGSR by jointly modeling causality and correlation relationship between items.
no code implementations • NeurIPS 2021 • Cong Geng, Jia Wang, Zhiyong Gao, Jes Frellsen, Søren Hauberg
Energy-based models (EBMs) provide an elegant framework for density estimation, but they are notoriously difficult to train.
3 code implementations • ICCV 2021 • Peng Zhou, Lingxi Xie, Bingbing Ni, Cong Geng, Qi Tian
The conditional generative adversarial network (cGAN) is a powerful tool of generating high-quality images, but existing approaches mostly suffer unsatisfying performance or the risk of mode collapse.
Ranked #8 on Conditional Image Generation on ImageNet 128x128
no code implementations • 23 Sep 2020 • Cong Geng, Jia Wang, Li Chen, Zhiyong Gao
Variational Autoencoder (VAE) and its variations are classic generative models by learning a low-dimensional latent representation to satisfy some prior distribution (e. g., Gaussian distribution).
no code implementations • 12 Feb 2020 • Cong Geng, Jia Wang, Li Chen, Wenbo Bao, Chu Chu, Zhiyong Gao
Based on this defined Riemannian metric, we introduce a constant speed loss and a minimizing geodesic loss to regularize the interpolation network to generate uniform interpolation along the learned geodesic on the manifold.
no code implementations • ICLR 2020 • Peng Zhou, Bingbing Ni, Lingxi Xie, Xiaopeng Zhang, Hang Wang, Cong Geng, Qi Tian
In the field of Generative Adversarial Networks (GANs), how to design a stable training strategy remains an open problem.
no code implementations • CVPR 2018 • Peng Zhou, Bingbing Ni, Cong Geng, Jianguo Hu, Yi Xu
Scale problem lies in the heart of object detection.