Search Results for author: Vahid Reza Khazaie

Found 7 papers, 3 papers with code

Are Out-of-Distribution Detection Methods Reliable?

no code implementations20 Nov 2022 Vahid Reza Khazaie, Anthony Wong, Mohammad Sabokrou

This paper establishes a novel evaluation framework for assessing the performance of out-of-distribution (OOD) detection in realistic settings.

Out-of-Distribution Detection Out of Distribution (OOD) Detection

Anomaly Detection with Adversarially Learned Perturbations of Latent Space

no code implementations3 Jul 2022 Vahid Reza Khazaie, Anthony Wong, John Taylor Jewell, Yalda Mohsenzadeh

The Adversarial Distorter is a convolutional encoder that learns to produce effective perturbations and the autoencoder is a deep convolutional neural network that aims to reconstruct the images from the perturbed latent feature space.

Unsupervised Anomaly Detection

Augment to Detect Anomalies with Continuous Labelling

no code implementations3 Jul 2022 Vahid Reza Khazaie, Anthony Wong, Yalda Mohsenzadeh

Therefore, training a regressor on these augmented samples will result in more separable distributions of labels for normal and real anomalous data points.

Anomaly Detection Image Augmentation

OLED: One-Class Learned Encoder-Decoder Network with Adversarial Context Masking for Novelty Detection

1 code implementation27 Mar 2021 John Taylor Jewell, Vahid Reza Khazaie, Yalda Mohsenzadeh

In particular, context autoencoders have been successful in the novelty detection task because of the more effective representations they learn by reconstructing original images from randomly masked images.

Anomaly Detection

Latent Vector Recovery of Audio GANs

no code implementations16 Oct 2020 Andrew Keyes, Nicky Bayat, Vahid Reza Khazaie, Yalda Mohsenzadeh

Through our deep neural network based method of training on real and synthesized audio, we are able to predict a latent vector that corresponds to a reasonable reconstruction of real audio.

Inverse mapping of face GANs

1 code implementation11 Sep 2020 Nicky Bayat, Vahid Reza Khazaie, Yalda Mohsenzadeh

The vast majority of studies on latent vector recovery perform well only on generated images, we argue that our method can be used to determine a mapping between real human faces and latent-space vectors that contain most of the important face style details.

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