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Found 18 papers, 6 papers with code
Biased Binary Attribute Classifiers Ignore the Majority Classes
In this paper, we extend gradient-based CAM techniques to work with binary classifiers and visualize the active regions for binary facial attribute classifiers.
Open-Set Face Recognition with Maximal Entropy and Objectosphere Loss
MEL modifies the traditional Cross-Entropy loss in favor of increasing the entropy for negative samples and attaches a penalty to known target classes in pursuance of gallery specialization.
Open-set Face Recognition with Neural Ensemble, Maximal Entropy Loss and Feature Augmentation
Open-set face recognition refers to a scenario in which biometric systems have incomplete knowledge of all existing subjects.
Bridging Trustworthiness and Open-World Learning: An Exploratory Neural Approach for Enhancing Interpretability, Generalization, and Robustness
As researchers strive to narrow the gap between machine intelligence and human through the development of artificial intelligence technologies, it is imperative that we recognize the critical importance of trustworthiness in open-world, which has become ubiquitous in all aspects of daily life for everyone.
Consistency and Accuracy of CelebA Attribute Values
Two annotators independently assigning attribute values shows that only 12 of 40 common attributes are assigned values with >= 95% consistency, and three (high cheekbones, pointed nose, oval face) have essentially random consistency.
Large-Scale Open-Set Classification Protocols for ImageNet
Open-Set Classification (OSC) intends to adapt closed-set classification models to real-world scenarios, where the classifier must correctly label samples of known classes while rejecting previously unseen unknown samples.
Eight Years of Face Recognition Research: Reproducibility, Achievements and Open Issues
With the popularity of deep learning and its capability to solve a huge variety of different problems, face recognition researchers have concentrated effort on creating better models under this paradigm.
Reducing Network Agnostophobia
Agnostophobia, the fear of the unknown, can be experienced by deep learning engineers while applying their networks to real-world applications.
Facial Attributes: Accuracy and Adversarial Robustness
Facial attributes, emerging soft biometrics, must be automatically and reliably extracted from images in order to be usable in stand-alone systems.
Unconstrained Face Detection and Open-Set Face Recognition Challenge
Face detection and recognition benchmarks have shifted toward more difficult environments.
Adversarial Robustness: Softmax versus Openmax
Deep neural networks (DNNs) provide state-of-the-art results on various tasks and are widely used in real world applications.
Toward Open-Set Face Recognition
In this paper, we address the widespread misconception that thresholding verification-like scores is a good way to solve the open-set face identification problem, by formulating an open-set face identification protocol and evaluating different strategies for assessing similarity.
LOTS about Attacking Deep Features
We demonstrate that iterative LOTS is very effective and show that systems utilizing deep features are easier to attack than the end-to-end network.
AFFACT - Alignment-Free Facial Attribute Classification Technique
Using an ensemble of three ResNets, we obtain the new state-of-the-art facial attribute classification error of 8. 00% on the aligned images of the CelebA dataset.
Are Accuracy and Robustness Correlated?
In this paper, we perform experiments on various adversarial example generation approaches with multiple deep convolutional neural networks including Residual Networks, the best performing models on ImageNet Large-Scale Visual Recognition Challenge 2015.
Are Facial Attributes Adversarially Robust?
We show that FFA generates more adversarial examples than other related algorithms, and that DCNNs for certain attributes are generally robust to adversarial inputs, while DCNNs for other attributes are not.
MOON: A Mixed Objective Optimization Network for the Recognition of Facial Attributes
Unfortunately, it can be difficult to apply joint optimization to DCNNs when training data is imbalanced, and re-balancing multi-label data directly is structurally infeasible, since adding/removing data to balance one label will change the sampling of the other labels.
A Survey of Stealth Malware: Attacks, Mitigation Measures, and Steps Toward Autonomous Open World Solutions
While machine learning offers promising potential for increasingly autonomous solutions with improved generalization to new malware types, both at the network level and at the host level, our findings suggest that several flawed assumptions inherent to most recognition algorithms prevent a direct mapping between the stealth malware recognition problem and a machine learning solution.
