As Deep Neural Networks (DNNs) have demonstrated superhuman performance in many computer vision tasks, there is an increasing interest in revealing the complex internal mechanisms of DNNs. In this paper, we propose Relative Attributing Propagation (RAP), which decomposes the output predictions of DNNs with a new perspective that precisely separates the positive and negative attributions. By identifying the fundamental causes of activation and the proper inversion of relevance, RAP allows each neuron to be assigned an actual contribution to the output. Furthermore, we devise pragmatic methods to handle the effect of bias and batch normalization properly in the attributing procedures. Therefore, our method makes it possible to interpret various kinds of very deep neural network models with clear and attentive visualizations of positive and negative attributions. By utilizing the region perturbation method and comparing the distribution of attributions for a quantitative evaluation, we verify the correctness of our RAP whether the positive and negative attributions correctly account for each meaning. The positive and negative attributions propagated by RAP show the characteristics of vulnerability and robustness to the distortion of the corresponding pixels, respectively. We apply RAP to DNN models; VGG-16, ResNet-50 and Inception-V3, demonstrating its generation of more intuitive and improved interpretation compared to the existing attribution methods.
Guided BackProp @cite_2 and Integrated Gradients @cite_21 each compute the single and average partial derivatives of the output to attribute the prediction of a DNN. Deep Taylor Decomposition @cite_31 is an extension of LRP for interpreting the decision of a DNN by decomposing the activation of a neuron in terms of the contributions from its inputs. DeepLIFT @cite_18 decomposes the output prediction by assigning the differences of contribution scores between the activation of each neuron to its reference activation. @cite_22 approached the problem of the attribution value from a theoretical perspective and formally proved the conditions of equivalence and approximation between four attribution methods: Guided Input, Integrated Gradients, LRP and DeepLIFT.
@cite_18 @cite_22 @cite_21 @cite_2 @cite_31
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The purported "black box" nature of neural networks is a barrier to adoption in applications where interpretability is essential. Here we present DeepLIFT (Deep Learning Important FeaTures), a method for decomposing the output prediction of a neural network on a specific input by backpropagating the contributions of all neurons in the network to every feature of the input. DeepLIFT compares the activation of each neuron to its 'reference activation' and assigns contribution scores according to the difference. By optionally giving separate consideration to positive and negative contributions, DeepLIFT can also reveal dependencies which are missed by other approaches. Scores can be computed efficiently in a single backward pass. We apply DeepLIFT to models trained on MNIST and simulated genomic data, and show significant advantages over gradient-based methods. Video tutorial: http: goo.gl qKb7pL, code: http: goo.gl RM8jvH. Nonlinear methods such as Deep Neural Networks (DNNs) are the gold standard for various challenging machine learning problems such as image recognition. Although these methods perform impressively well, they have a significant disadvantage, the lack of transparency, limiting the interpretability of the solution and thus the scope of application in practice. Especially DNNs act as black boxes due to their multilayer nonlinear structure. In this paper we introduce a novel methodology for interpreting generic multilayer neural networks by decomposing the network classification decision into contributions of its input elements. Although our focus is on image classification, the method is applicable to a broad set of input data, learning tasks and network architectures. Our method called deep Taylor decomposition efficiently utilizes the structure of the network by backpropagating the explanations from the output to the input layer. We evaluate the proposed method empirically on the MNIST and ILSVRC data sets. HighlightsA novel method to explain nonlinear classification decisions in terms of input variables is introduced.The method is based on Taylor expansions and decomposes the output of a deep neural network in terms of input variables.The resulting deep Taylor decomposition can be applied directly to existing neural networks without retraining.The method is tested on two large-scale neural networks for image classification: BVLC CaffeNet and GoogleNet. The purported "black box"' nature of neural networks is a barrier to adoption in applications where interpretability is essential. Here we present DeepLIFT (Deep Learning Important FeaTures), a method for decomposing the output prediction of a neural network on a specific input by backpropagating the contributions of all neurons in the network to every feature of the input. DeepLIFT compares the activation of each neuron to its 'reference activation' and assigns contribution scores according to the difference. By optionally giving separate consideration to positive and negative contributions, DeepLIFT can also reveal dependencies which are missed by other approaches. Scores can be computed efficiently in a single backward pass. We apply DeepLIFT to models trained on MNIST and simulated genomic data, and show significant advantages over gradient-based methods. A detailed video tutorial on the method is at this http URL and code is at this http URL. Recent advances in deep neural networks (DNNs) owe their success to training algorithms that use backpropagation and gradient-descent. Backpropagation, while highly effective on von Neumann architectures, becomes inefficient when scaling to large networks. Commonly referred to as the weight transport problem, each neuron's dependence on the weights and errors located deeper in the network require exhaustive data movement which presents a key problem in enhancing the performance and energy-efficiency of machine-learning hardware. In this work, we propose a bio-plausible alternative to backpropagation drawing from advances in feedback alignment algorithms in which the error computation at a single synapse reduces to the product of three scalar values, satisfying the three factor rule. Using a sparse feedback matrix, we show that a neuron needs only a fraction of the information previously used by the feedback alignment algorithms to yield results which are competitive with backpropagation. Consequently, memory and compute can be partitioned and distributed whichever way produces the most efficient forward pass so long as a single error can be delivered to each neuron. We evaluate our algorithm using standard data sets, including ImageNet, to address the concern of scaling to challenging problems. Our results show orders of magnitude improvement in data movement and @math improvement in multiply-and-accumulate operations over backpropagation. All the code and results are available under https: github.com bcrafton ssdfa.