Search Results for author: Cong Geng

Found 7 papers, 1 papers with code

Causality and Correlation Graph Modeling for Effective and Explainable Session-based Recommendation

no code implementations26 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.

Session-Based Recommendations

Bounds all around: training energy-based models with bidirectional bounds

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.

Density Estimation

Omni-GAN: On the Secrets of cGANs and Beyond

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.

Conditional Image Generation

Generative Model without Prior Distribution Matching

no code implementations23 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).

Uniform Interpolation Constrained Geodesic Learning on Data Manifold

no code implementations12 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.

Translation

Wasserstein-Bounded Generative Adversarial Networks

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.

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