Search Results for author: Mohit Prabhushankar

Found 18 papers, 6 papers with code

DECAL: DEployable Clinical Active Learning

no code implementations21 Jun 2022 Yash-yee Logan, Mohit Prabhushankar, Ghassan AlRegib

Hence, active learning techniques that are developed for natural images are insufficient for handling medical data.

Active Learning

Gradient-Based Adversarial and Out-of-Distribution Detection

no code implementations16 Jun 2022 Jinsol Lee, Mohit Prabhushankar, Ghassan AlRegib

We propose to utilize gradients for detecting adversarial and out-of-distribution samples.

Out-of-Distribution Detection

Explanatory Paradigms in Neural Networks

1 code implementation24 Feb 2022 Ghassan AlRegib, Mohit Prabhushankar

With $P$ as the prediction from a neural network, these questions are `Why P?

Decision Making

Contrastive Reasoning in Neural Networks

no code implementations23 Mar 2021 Mohit Prabhushankar, Ghassan AlRegib

In this paper, we formalize the structure of contrastive reasoning and propose a methodology to extract a neural network's notion of contrast.

Object Recognition

Extracting Causal Visual Features for Limited label Classification

no code implementations23 Mar 2021 Mohit Prabhushankar, Ghassan AlRegib

Neural networks trained to classify images do so by identifying features that allow them to distinguish between classes.

Classification General Classification

Novelty Detection Through Model-Based Characterization of Neural Networks

no code implementations13 Aug 2020 Gukyeong Kwon, Mohit Prabhushankar, Dogancan Temel, Ghassan AlRegib

To articulate the significance of the model perspective in novelty detection, we utilize backpropagated gradients.

Implicit Saliency in Deep Neural Networks

no code implementations4 Aug 2020 Yutong Sun, Mohit Prabhushankar, Ghassan AlRegib

In this paper, we show that existing recognition and localization deep architectures, that have not been exposed to eye tracking data or any saliency datasets, are capable of predicting the human visual saliency.

Saliency Detection

Contrastive Explanations in Neural Networks

2 code implementations1 Aug 2020 Mohit Prabhushankar, Gukyeong Kwon, Dogancan Temel, Ghassan AlRegib

Current modes of visual explanations answer questions of the form $`Why \text{ } P?'$.

Image Quality Assessment

Characterizing Missing Information in Deep Networks Using Backpropagated Gradients

no code implementations ICLR 2020 Gukyeong Kwon, Mohit Prabhushankar, Dogancan Temel, Ghassan AlRegib

To complement the learned information from activation-based representation, we propose utilizing a gradient-based representation that explicitly focuses on missing information.

Anomaly Detection

Distorted Representation Space Characterization Through Backpropagated Gradients

2 code implementations27 Aug 2019 Gukyeong Kwon, Mohit Prabhushankar, Dogancan Temel, Ghassan AlRegib

In this paper, we utilize weight gradients from backpropagation to characterize the representation space learned by deep learning algorithms.

General Classification Image Quality Assessment

Semantically Interpretable and Controllable Filter Sets

no code implementations17 Feb 2019 Mohit Prabhushankar, Gukyeong Kwon, Dogancan Temel, Ghassan AlRegib

In this paper, we generate and control semantically interpretable filters that are directly learned from natural images in an unsupervised fashion.

Image Quality Assessment

Generating Adaptive and Robust Filter Sets Using an Unsupervised Learning Framework

no code implementations21 Nov 2018 Mohit Prabhushankar, Dogancan Temel, Ghassan AlRegib

While assessing image quality, the filters need to capture perceptual differences based on dissimilarities between a reference image and its distorted version.

Image Quality Assessment

CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign Recognition

1 code implementation7 Dec 2017 Dogancan Temel, Gukyeong Kwon, Mohit Prabhushankar, Ghassan AlRegib

We benchmark the performance of existing solutions in real-world scenarios and analyze the performance variation with respect to challenging conditions.

Data Augmentation Traffic Sign Recognition

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