Search Results for author: Saeid Asgari Taghanaki

Found 12 papers, 4 papers with code

Radial Basis Feature Transformation to Arm CNNs Against Adversarial Attacks

no code implementations ICLR 2019 Saeid Asgari Taghanaki, Shekoofeh Azizi, Ghassan Hamarneh

The linear and non-flexible nature of deep convolutional models makes them vulnerable to carefully crafted adversarial perturbations.

Image Classification

Robust Representation Learning via Perceptual Similarity Metrics

no code implementations11 Jun 2021 Saeid Asgari Taghanaki, Kristy Choi, Amir Khasahmadi, Anirudh Goyal

A fundamental challenge in artificial intelligence is learning useful representations of data that yield good performance on a downstream task, without overfitting to spurious input features.

Out-of-Distribution Generalization Representation Learning

PointMask: Towards Interpretable and Bias-Resilient Point Cloud Processing

1 code implementation9 Jul 2020 Saeid Asgari Taghanaki, Kaveh Hassani, Pradeep Kumar Jayaraman, Amir Hosein Khasahmadi, Tonya Custis

We show that coupling a PointMask layer with an arbitrary model can discern the points in the input space which contribute the most to the prediction score, thereby leading to interpretability.

3D Point Cloud Classification Robust classification

Jigsaw-VAE: Towards Balancing Features in Variational Autoencoders

no code implementations12 May 2020 Saeid Asgari Taghanaki, Mohammad Havaei, Alex Lamb, Aditya Sanghi, Ara Danielyan, Tonya Custis

The latent variables learned by VAEs have seen considerable interest as an unsupervised way of extracting features, which can then be used for downstream tasks.

Signed Input Regularization

no code implementations16 Nov 2019 Saeid Asgari Taghanaki, Kumar Abhishek, Ghassan Hamarneh

To test the effectiveness of the proposed idea and compare it with other competing methods, we design several test scenarios, such as classification performance, uncertainty, out-of-distribution, and robustness analyses.

Data Augmentation

Deep Semantic Segmentation of Natural and Medical Images: A Review

no code implementations16 Oct 2019 Saeid Asgari Taghanaki, Kumar Abhishek, Joseph Paul Cohen, Julien Cohen-Adad, Ghassan Hamarneh

The semantic image segmentation task consists of classifying each pixel of an image into an instance, where each instance corresponds to a class.

Medical Image Segmentation Scene Understanding +1

InfoMask: Masked Variational Latent Representation to Localize Chest Disease

no code implementations28 Mar 2019 Saeid Asgari Taghanaki, Mohammad Havaei, Tess Berthier, Francis Dutil, Lisa Di Jorio, Ghassan Hamarneh, Yoshua Bengio

The scarcity of richly annotated medical images is limiting supervised deep learning based solutions to medical image analysis tasks, such as localizing discriminatory radiomic disease signatures.

Multiple Instance Learning

Vulnerability Analysis of Chest X-Ray Image Classification Against Adversarial Attacks

no code implementations9 Jul 2018 Saeid Asgari Taghanaki, Arkadeep Das, Ghassan Hamarneh

Recently, there have been several successful deep learning approaches for automatically classifying chest X-ray images into different disease categories.

General Classification Image Classification

Select, Attend, and Transfer: Light, Learnable Skip Connections

no code implementations14 Apr 2018 Saeid Asgari Taghanaki, Aicha Bentaieb, Anmol Sharma, S. Kevin Zhou, Yefeng Zheng, Bogdan Georgescu, Puneet Sharma, Sasa Grbic, Zhoubing Xu, Dorin Comaniciu, Ghassan Hamarneh

Skip connections in deep networks have improved both segmentation and classification performance by facilitating the training of deeper network architectures, and reducing the risks for vanishing gradients.

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