Work

Greedy auto-augmentation for n-shot learning using deep neural networks

Year: 2020

Type: article

Source: Neural Networks

DOI: 10.1016/j.neunet.2020.11.015

Authors: Alireza Naghizadeh, Dimitris N. Metaxas, Dongfang Liu

Institutions: Rutgers, The State University of New Jersey, Rutgers New Jersey Medical School

Language: English

Topic: Domain Adaptation and Few-Shot Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funder: National Science Foundation

Awards: CHS: Medium: Data Driven Biomechanically Accurate Modeling of Human Gait on Unconstrained Terrain, AitF: Collaborative Research: Topological Algorithms for 3D/4D Cardiac Images: Understanding Complex and Dynamic Structures, G4186481584, S&AS: FND: COLLAB: Adaptable Vehicular Sensing and Control for Fleet-Oriented Systems in Smart Cities, Phase 1 IUCRC Rutgers-New Brunswick: Center for Accelerated Real Time Analytics (CARTA), CHS: Medium: Collaborative Research: Scalable Integration of Data-Driven and Model-Based Methods for Large Vocabulary Sign Recognition and Search

OpenAlex ID: W3112873620

FWCI: 1.473

Cites: 64

Cited by: 20

Related to: 10