DASCN uses a unified primal-dual GAN architecture to generate semantics-consistent visual features for generalized zero-shot learning, claiming state-of-the-art gains.
Visualizing data using t-sne
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2019 4verdicts
UNVERDICTED 4representative citing papers
A semi-supervised feature-level attribute manipulation method for fashion images that matches manipulated feature distributions to real features to support both instance retrieval and attribute editing.
TSPG applies conditional GANs to generate realistic transcriptome perturbations that mimic source-to-target gene expression state transitions and highlight biologically enriched genes.
NANG uses adversarial learning to generate unobserved node attributes from graph structure via a shared latent space.
citing papers explorer
-
Dual Adversarial Semantics-Consistent Network for Generalized Zero-Shot Learning
DASCN uses a unified primal-dual GAN architecture to generate semantics-consistent visual features for generalized zero-shot learning, claiming state-of-the-art gains.
-
Semi-supervised Feature-Level Attribute Manipulation for Fashion Image Retrieval
A semi-supervised feature-level attribute manipulation method for fashion images that matches manipulated feature distributions to real features to support both instance retrieval and attribute editing.
-
Cellular State Transformations using Generative Adversarial Networks
TSPG applies conditional GANs to generate realistic transcriptome perturbations that mimic source-to-target gene expression state transitions and highlight biologically enriched genes.
-
Node Attribute Generation on Graphs
NANG uses adversarial learning to generate unobserved node attributes from graph structure via a shared latent space.