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Found 9 papers, 1 papers with code
Improving Adversarial Energy-Based Model via Diffusion Process
Generative models have shown strong generation ability while efficient likelihood estimation is less explored.
Session-Based Recommendation by Exploiting Substitutable and Complementary Relationships from Multi-behavior Data
Session-based recommendation (SR) aims to dynamically recommend items to a user based on a sequence of the most recent user-item interactions.
Causality and Correlation Graph Modeling for Effective and Explainable Session-based Recommendation
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.
Bounds all around: training energy-based models with bidirectional bounds
Energy-based models (EBMs) provide an elegant framework for density estimation, but they are notoriously difficult to train.
Omni-GAN: On the Secrets of cGANs and Beyond
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
Generative Model without Prior Distribution Matching
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
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.
Wasserstein-Bounded Generative Adversarial Networks
In the field of Generative Adversarial Networks (GANs), how to design a stable training strategy remains an open problem.
