26 citations · 79 across the 9 of their papers we have counts for
9 papers
Self-supervised Robust Object Detectors from Partially Labelled Datasets
Mahdieh Abbasi, Denis Laurendeau, Christian Gagne
In the object detection task, merging various datasets from similar contexts but with different sets of Objects of Interest (OoI) is an inexpensive way (in terms of labor cost) for…
Toward Adversarial Robustness by Diversity in an Ensemble of Specialized Deep Neural Networks
Mahdieh Abbasi, Arezoo Rajabi, Christian Gagne +1
We aim at demonstrating the influence of diversity in the ensemble of CNNs on the detection of black-box adversarial instances and hardening the generation of white-box adversarial…
Toward Metrics for Differentiating Out-of-Distribution Sets
Mahdieh Abbasi, Changjian Shui, Arezoo Rajabi +2
Vanilla CNNs, as uncalibrated classifiers, suffer from classifying out-of-distribution (OOD) samples nearly as confidently as in-distribution samples. To tackle this challenge, som…
A Principled Approach for Learning Task Similarity in Multitask Learning
Changjian Shui, Mahdieh Abbasi, Louis-Émile Robitaille +2
Multitask learning aims at solving a set of related tasks simultaneously, by exploiting the shared knowledge for improving the performance on individual tasks. Hence, an important…
Controlling Over-generalization and its Effect on Adversarial Examples Generation and Detection
Mahdieh Abbasi, Arezoo Rajabi, Azadeh Sadat Mozafari +2
Convolutional Neural Networks (CNNs) significantly improve the state-of-the-art for many applications, especially in computer vision. However, CNNs still suffer from a tendency to…
Towards Dependable Deep Convolutional Neural Networks (CNNs) with Out-distribution Learning
Mahdieh Abbasi, Arezoo Rajabi, Christian Gagné +1
Detection and rejection of adversarial examples in security sensitive and safety-critical systems using deep CNNs is essential. In this paper, we propose an approach to augment CNN…
Out-distribution training confers robustness to deep neural networks
Mahdieh Abbasi, Christian Gagné
The easiness at which adversarial instances can be generated in deep neural networks raises some fundamental questions on their functioning and concerns on their use in critical sy…
Robustness to Adversarial Examples through an Ensemble of Specialists
Mahdieh Abbasi, Christian Gagné
We are proposing to use an ensemble of diverse specialists, where speciality is defined according to the confusion matrix. Indeed, we observed that for adversarial instances origin…
Alternating Direction Method of Multipliers for Sparse Convolutional Neural Networks
Farkhondeh Kiaee, Christian Gagné, Mahdieh Abbasi
The storage and computation requirements of Convolutional Neural Networks (CNNs) can be prohibitive for exploiting these models over low-power or embedded devices. This paper reduc…