Source-linked AI summary
iNNvestigate neural networks!
Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer, Miriam Hägele, Kristof T. Schütt, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller, Sven Dähne, Pieter-Jan Kindermans
TL;DR
Neural networks are widely used in critical processes, but their black-box behavior and the lack of reference implementations hinder systematic comparison of analysis methods. iNNvestigate addresses this with a common interface, broad method implementations, and tools demonstrated across state-of-the-art image-classification architectures. The library is intended to facilitate neural-network analysis and research in application domains including drug design and medical imaging.
Problem
Neural networks support critical processes yet are often treated as black boxes, while lacking reference implementations makes systematic comparison of analysis methods difficult.
Method
iNNvestigate provides a common interface and implementations for many analysis methods, plus tools for training, quantitative comparison, and model demonstrations.
Results
The library demonstrates image-classification analyses across a variety of state-of-the-art neural-network architectures.
Takeaways & Limitations
iNNvestigate makes it easier to analyze neural-network predictions and compare methods, supporting research in areas such as drug design and medical image analysis.
Takeaways & Limitations
The authors avoid the terms “explaining” and “interpreting” to prevent assumptions beyond the algorithms’ design choices, using “analyzing” instead.
Abstract
from arXiv · showhide
In recent years, deep neural networks have revolutionized many application domains of machine learning and are key components of many critical decision or predictive processes. Therefore, it is crucial that domain specialists can understand and analyze actions and pre- dictions, even of the most complex neural network architectures. Despite these arguments neural networks are often treated as black boxes. In the attempt to alleviate this short- coming many analysis methods were proposed, yet the lack of reference implementations often makes a systematic comparison between the methods a major effort. The presented library iNNvestigate addresses this by providing a common interface and out-of-the- box implementation for many analysis methods, including the reference implementation for PatternNet and PatternAttribution as well as for LRP-methods. To demonstrate the versatility of iNNvestigate, we provide an analysis of image classifications for variety of state-of-the-art neural network architectures.
1. Introduction
iNNvestigate responds to the difficulty of understanding and systematically comparing neural-network analysis methods by providing shared implementations and interfaces. It also demonstrates these tools on state-of-the-art image-classification networks.
- Deep neural networks increasingly support practical and critical processes, making their actions and predictions important for users to understand.
- Neural networks remain black boxes, and the field lacks clear empirical evidence about which analysis method best addresses this problem.
- iNNvestigate provides a common interface for analysis methods across a broad class of neural networks, enabling straightforward method instantiation and qualitative comparison.
- The library supports the listed analysis methods, including first reference implementations for PatternNet and PatternAttribution and an extended implementation for LRP.
- Its modular implementation expresses analyses through layer-wise forward and backward computations, reducing redundancy and easing integration of new methods.
- Examples analyze image classifications across varied state-of-the-art neural-network architectures.
2. Library
The library combines a simple analyzer workflow with broad algorithm and architecture support, modular propagation components, data-dependent pattern training, and quantitative comparison tools.
- Interface: Users instantiate an analyzer with a Keras model, fit it when needed, and analyze predictions through a common workflow.The documented code creates an analyzer, optionally fits it on training data, and applies it to test data.
- Implemented methods: At publication, supported methods included gradient, smoothing, attribution, DeepTaylor, and several LRP variants.
- Documentation: Examples cover VGG16, VGG19, InceptionV3, ResNet50, InceptionResNetV2, DenseNet, and NASNet architectures.
- Documentation: Figure 1 reports method outputs across neural networks, leaving blanks where a method does not yet support an architecture.
- Modular implementation: The modular core handles back-propagation from outputs to inputs and graph propagation, allowing developers to implement algorithm-specific changes without rewriting complex handling.
- Modular implementation: Wrappers extend analyzers; SmoothGrad combines Gaussian-noise smoothing with a gradient analyzer to average analysis outcomes.
- Training: PatternNet and PatternAttribution condition analyses on data distributions by identifying signal and noise directions for each neuron.Required patterns can be trained on large datasets such as ImageNet in less than an hour using one GPU.
- Quantitative evaluation: Perturbation analysis, also called PixelFlipping, provides a more objective comparison by testing how perturbing identified important regions affects classification.
3. Conclusion
iNNvestigate makes neural-network prediction analysis and method comparison easier through a common interface, broad implementations, training and comparison tools, and examples across state-of-the-art applications.
- iNNvestigate provides reference implementations for PatternNet, PatternAttribution, and LRP alongside examples covering many state-of-the-art applications.
- The authors expect the library to support research on analyzing machine learning in areas such as drug design and medical image analysis.