Corpus ID: 236140673

Believe The HiPe: Hierarchical Perturbation for Fast, Robust and Model-Agnostic Explanations

  title={Believe The HiPe: Hierarchical Perturbation for Fast, Robust and Model-Agnostic Explanations},
  author={Jessica Cooper and Ognjen Arandjelovic and David Harrison},
Understanding the predictions made by Artificial Intelligence (AI) systems is becoming more and more important as deep learning models are used for increasingly complex and high-stakes tasks. Saliency mapping – an easily interpretable visual attribution method – is one important tool for this, but existing formulations are limited by either computational cost or architectural constraints. We therefore propose Hierarchical Perturbation, a very fast and completely modelagnostic method for… Expand

Figures and Tables from this paper


Interpretable Explanations of Black Boxes by Meaningful Perturbation
  • Ruth C. Fong, A. Vedaldi
  • Computer Science, Mathematics
  • 2017 IEEE International Conference on Computer Vision (ICCV)
  • 2017
A general framework for learning different kinds of explanations for any black box algorithm is proposed and the framework to find the part of an image most responsible for a classifier decision is specialised. Expand
Sanity Checks for Saliency Maps
It is shown that some existing saliency methods are independent both of the model and of the data generating process, and methods that fail the proposed tests are inadequate for tasks that are sensitive to either data or model. Expand
Benchmarking Perturbation-based Saliency Maps for Explaining Deep Reinforcement Learning Agents
Four perturbation-based approaches to create saliency maps for Deep Reinforcement Learning agents trained on four different Atari 2600 games are compared using three computational metrics: dependence on the learned parameters of the agent, faithfulness to the agent’s reasoning and run-time. Expand
Evaluating Input Perturbation Methods for Interpreting CNNs and Saliency Map Comparison
It is shown that arguably neutral baseline images still impact the generated saliency maps and their evaluation with input perturbations and it is demonstrated that many choices of hyperparameters lead to the divergence ofsaliency maps generated by input perturbedations. Expand
Explanations for Attributing Deep Neural Network Predictions
This chapter introduces Meta-Predictors as Explanations, a principled framework for learning explanations for any black box algorithm, and Meaningful Perturbations, an instantiation of the paradigm applied to the problem of attribution. Expand
Understanding Deep Networks via Extremal Perturbations and Smooth Masks
Some of the shortcomings of existing approaches to perturbation analysis are discussed and the concept of extremal perturbations are introduced, which are theoretically grounded and interpretable and allow us to remove all tunable weighing factors from the optimization problem. Expand
RISE: Randomized Input Sampling for Explanation of Black-box Models
The problem of Explainable AI for deep neural networks that take images as input and output a class probability is addressed and an approach called RISE that generates an importance map indicating how salient each pixel is for the model's prediction is proposed. Expand
Saliency Maps Generation for Automatic Text Summarization
This paper applies Layer-Wise Relevance Propagation (LRP) to a sequence-to-sequence attention model trained on a text summarization dataset and shows that the saliency maps obtained sometimes capture the real use of the input features by the network, and sometimes do not. Expand
Visualizing and Understanding Convolutional Networks
A novel visualization technique is introduced that gives insight into the function of intermediate feature layers and the operation of the classifier in large Convolutional Network models, used in a diagnostic role to find model architectures that outperform Krizhevsky et al on the ImageNet classification benchmark. Expand
Real Time Image Saliency for Black Box Classifiers
A masking model is trained to manipulate the scores of the classifier by masking salient parts of the input image to generalise well to unseen images and requires a single forward pass to perform saliency detection, therefore suitable for use in real-time systems. Expand