0% Complete
Home
/
12th International Conference on Computer and Knowledge Engineering
Averting Mode Collapse for Generative Zero-Shot Learning
Authors :
Shayan Ramazi
1
Setare Shabani
2
1- Cyberspace research institute, Shahid Beheshti University, G.C., Tehran, Iran
2- Department of Information Technology, K. N. Toosi University of Technology, Tehran, Iran
Keywords :
Generative Zero-shot learning،Zero-shot learning(ZSL)،image classification،Generalized Zero-shot learning(GZSL)،Generative Adversarial Networks(GAN)
Abstract :
Zero-shot learning is a promising method for supervised learning methods when there is no labeled data from some classes. In fact, in zero-shot learning, the model classifies the data with no labeled sample present in the training phase. Recently, researchers have shown that the use of conditional adversarial generative networks can be a solution to generate synthetic data from classes that do not have labeled data. In this paper, we try to improve the quality and variety of data generated from classes with no labeled sample through an adversarial generative network by introducing a new architecture. In addition to the changes made in the training phase of the adversarial generator network, a regulator has also been utilized to ensure the diversity of generated data. This regulator also prevents mode collapse. The proposed architecture is assessed on the Animals With Attributes (AWA) dataset. Classification accuracy in ZSL and GZSL settings improved by 1.7% and 2.3% in comparison to the state-of-the-art results.
Papers List
List of archived papers
Sensitivity Reliability Analysis of Power Distribution Networks Using Fuzzy Logic
Mohammed Wadi - Wisam Elmasry - Ismail Kucuk - Hossein Shahinzadeh
New Design of Efficient Reversible Quantum Saturation Adder
Negin Mashayekhi - Mohammad Reza Reshadinezhad - Shekoofeh Moghimi
Weakly Supervised Convolutional Neural Network for Automatic Gleason Grading of Prostate Cancer
Maryam Kamareh - Mohammad Sadegh Helfroush - Kamran Kazemi
Decentralized Federated Learning in IoT Environments: A Hierarchical Approach
Majid Mohammadpour - Seyedakbar Mostafavi
Financial Market Prediction Using Deep Neural Networks with Hardware Acceleration
Dara Rahmati - Mohammad Hadi Foroughi - Ali Bagherzadeh - Mehdi Foroughi - Saeid Gorgin
AVID: A VARIATIONAL INFERENCE DELIBERATION FOR META-LEARNING
Alireza Javaheri - Arsham Gholamzadeh Khoee - Saeed Reza Kheradpisheh - Hadi Farahani - Mohammad Ganjtabesh
Vision-Based Obstacle Avoidance in Drone Navigation using Deep Reinforcement Learning
Pooyan Rahmanzadeh Gervi - Ahad Harati - Sayed Kamaledin Ghiasi-Shirazi
A Weighted TF-IDF-based Approach for Authorship Attribution
Ali Abedzadeh - Reza Ramezani - Afsaneh Fatemi
An Ensemble CNN for Brain Age Estimation based on Hippocampal Region Applicable to Alzheimer's Diagnosis
Zahra Qodrati - Seyedeh Masoumeh Taji - Habibollah Danyali - Kamran Kazemi
SASIAF, An Scalable Accelerator For Seismic Imaging on Amazon AWS FPGAs
Mostafa Koraei - S.Omid Fatemi
more
Samin Hamayesh - Version 41.3.1