Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
A Deep Dive into Adversarial Robustness in Zero-Shot Learning
Date
2020-08-23
Author
Yücel, Mehmet Kerim
Cinbiş, Ramazan Gökberk
Duygulu Şahin, Pınar
Metadata
Show full item record
Item Usage Stats
161
views
0
downloads
Cite This
Machine learning (ML) systems have introduced significant advances in various fields, due to the introduction of highly complex models. Despite their success, it has been shown multiple times that machine learning models are prone to imperceptible perturbations that can severely degrade their accuracy. So far, existing studies have primarily focused on models where supervision across all classes were available. In constrast, Zero-shot Learning (ZSL) and Generalized Zero-shot Learning (GZSL) tasks inherently lack supervision across all classes. In this paper, we present a study aimed on evaluating the adversarial robustness of ZSL and GZSL models. We leverage the well-established label embedding model and subject it to a set of established adversarial attacks and defenses across multiple datasets. In addition to creating possibly the first benchmark on adversarial robustness of ZSL models, we also present analyses on important points that require attention for better interpretation of ZSL robustness results. We hope these points, along with the benchmark, will help researchers establish a better understanding what challenges lie ahead and help guide their work.
URI
https://www.springer.com/gp/book/9783030664145
https://hdl.handle.net/11511/71443
Collections
Department of Computer Engineering, Conference / Seminar
Suggestions
OpenMETU
Core
A mathematical contribution of statistical learning and continuous optimization using infinite and semi-infinite programming to computational statistics
Özöğür-Akyüz, Süreyya; Weber, Gerhard Wilhelm; Department of Scientific Computing (2009)
A subfield of artificial intelligence, machine learning (ML), is concerned with the development of algorithms that allow computers to “learn”. ML is the process of training a system with large number of examples, extracting rules and finding patterns in order to make predictions on new data points (examples). The most common machine learning schemes are supervised, semi-supervised, unsupervised and reinforcement learning. These schemes apply to natural language processing, search engines, medical diagnosis,...
The Effect of Loss Functions on the Deep Learning Modeling for the Flow Field Predictions Around Airfoils
Doğan, Ali; Duru, Cihat; Alemdar, Hande; Baran, Özgür Uğraş (2021-09-10)
CNNFOIL is a CNN-based machine learning tool that solves flow around the airfoil with a machine learning methodology. CNNFOIL, which is being developed by our research group, can predict flowfield around airfoils from different families at transonic regimes. We have improved the training process and accuracy of the CNNFOIL solver by implementing new loss functions. In this study, the effects of an L2 -based loss function, a physics-informed loss function based on continuity equation and a gradient differenc...
A new likelihood approach to autonomous multiple model estimation
Söken, Halil Ersin (Elsevier BV, 2020-04-01)
This paper presents an autonomous multiple model (AMM) estimation algorithm for hybrid systems with sudden changes in their parameters. Estimates of Kalman filters (KFs) that are tuned and employed for different system modes are merged based on a newly defined likelihood function without any necessity for filter interaction. The proposed likelihood function is composed of two measures, the filter agility measure and the steady-state error measure. These measures are derived based on filter adaptation rules....
Computational representation of protein sequences for homology detection and classification
Oğul, Hasan; Mumcuoğlu, Ünal Erkan; Department of Information Systems (2006)
Machine learning techniques have been widely used for classification problems in computational biology. They require that the input must be a collection of fixedlength feature vectors. Since proteins are of varying lengths, there is a need for a means of representing protein sequences by a fixed-number of features. This thesis introduces three novel methods for this purpose: n-peptide compositions with reduced alphabets, pairwise similarity scores by maximal unique matches, and pairwise similarity scores by...
An analysis of stereo depth estimation utilizing attention mechanisms, self-supervised pose estimators & temporal predictions
Oğuzman, Utku; Alatan, Abdullah Aydın; Department of Electrical and Electronics Engineering (2022-5-18)
By the recent success of deep learning, real-world applications of stereo depth estimation algorithms attracted the interest of many researchers. Using the available datasets, synthetic or real-world, the researchers begin analyzing their ideas for practical applications. In this thesis, a thorough analysis is performed of such an aim. The state-of-the-art stereo depth estimation algorithms are tried to be improved by incorporating attention mechanisms to the current networks and better initialization strat...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
M. K. Yücel, R. G. Cinbiş, and P. Duygulu Şahin, “A Deep Dive into Adversarial Robustness in Zero-Shot Learning,” 2020, p. 3, Accessed: 00, 2021. [Online]. Available: https://www.springer.com/gp/book/9783030664145.