# Analyzing Classifiers: Fisher Vectors and Deep Neural Networks Source: [Analyzing Classifiers: Fisher Vectors and Deep Neural Networks]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:15:30.656Z Authors: Sebastian Bach, Alexander Binder, Gr\\'egoire Montavon, Klaus-Robert M\\"uller, Wojciech Samek Published: 2015-12-01T08:14:11.000Z ## Abstract Fisher Vector classifiers and Deep Neural Networks (DNNs) are popular and successful algorithms for solving image classification problems. However, both are generally considered \`black box' predictors as the non-linear transformations involved have so far prevented transparent and interpretable reasoning. Recently, a principled technique, Layer-wise Relevance Propagation (LRP), has been developed in order to better comprehend the inherent structured reasoning of complex nonlinear classification models such as Bag of Feature models or DNNs. In this paper we (1) extend the LRP framework also for Fisher Vector classifiers and then use it as analysis tool to (2) quantify the importance of context for classification, (3) qualitatively compare DNNs against FV classifiers in terms of important image regions and (4) detect potential flaws and biases in data. All experiments are performed on the PASCAL VOC 2007 data set. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()