Interpretability Techniques for Deep Learning

6 papers with code • 0 benchmarks • 0 datasets

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Greatest papers with code

What Do Compressed Deep Neural Networks Forget?

google-research/google-research 13 Nov 2019

However, this measure of performance conceals significant differences in how different classes and images are impacted by model compression techniques.

Fairness Interpretability Techniques for Deep Learning +4

Contextual Explanation Networks

alshedivat/cen ICLR 2018

Our results on image and text classification and survival analysis tasks demonstrate that CENs are not only competitive with the state-of-the-art methods but also offer additional insights behind each prediction, that can be valuable for decision support.

Image Classification Interpretability Techniques for Deep Learning +3

DISSECT: Disentangled Simultaneous Explanations via Concept Traversals

asmadotgh/dissect 31 May 2021

Explaining deep learning model inferences is a promising venue for scientific understanding, improving safety, uncovering hidden biases, evaluating fairness, and beyond, as argued by many scholars.

Fairness Interpretability Techniques for Deep Learning +1

Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images

soumickmj/diagnoPP 3 Jun 2020

The mean Micro-F1 score of the models for COVID-19 classifications ranges from 0. 66 to 0. 875, and is 0. 89 for the Ensemble of the network models.

COVID-19 Diagnosis General Classification +4

DeepNNK: Explaining deep models and their generalization using polytope interpolation

STAC-USC/DeepNNK_polytope_interpolation 20 Jul 2020

Modern machine learning systems based on neural networks have shown great success in learning complex data patterns while being able to make good predictions on unseen data points.

Interpretability Techniques for Deep Learning Interpretable Machine Learning +1