Search Results for author:
Found 6 papers, 5 papers with code
Fine-Grained Zero-Shot Learning with DNA as Side Information
Fine-grained zero-shot learning task requires some form of side-information to transfer discriminative information from seen to unseen classes.
Image Hashing by Minimizing Discrete Component-wise Wasserstein Distance
In this paper, we show that the high sample-complexity requirement often results in sub-optimal retrieval performance of the adversarial hashing methods.
Gradient Boosting Neural Networks: GrowNet
A novel gradient boosting framework is proposed where shallow neural networks are employed as ``weak learners''.
Open Set Authorship Attribution toward Demystifying Victorian Periodicals
Existing research in computational authorship attribution (AA) has primarily focused on attribution tasks with a limited number of authors in a closed-set configuration.
Bayesian Zero-Shot Learning
Object classes that surround us have a natural tendency to emerge at varying levels of abstraction.
Coupled IGMM-GANs for deep multimodal anomaly detection in human mobility data
In this paper we address two challenges that arise in the study of anomalous human trajectories: 1) a lack of ground truth data on what defines an anomaly and 2) the dependence of existing methods on significant pre-processing and feature engineering.
