  There are increasing demands for understanding deep neural networks' (DNNs)
behavior spurred by growing security and/or transparency concerns. Due to
multi-layer nonlinearity of the deep neural network architectures, explaining
DNN predictions still remains as an open problem, preventing us from gaining a
deeper understanding of the mechanisms. To enhance the explainability of DNNs,
we estimate the input feature's attributions to the prediction task using
divergence and flux. Inspired by the divergence theorem in vector analysis, we
develop a novel Negative Flux Aggregation (NeFLAG) formulation and an efficient
approximation algorithm to estimate attribution map. Unlike the previous
techniques, ours doesn't rely on fitting a surrogate model nor need any path
integration of gradients. Both qualitative and quantitative experiments
demonstrate a superior performance of NeFLAG in generating more faithful
attribution maps than the competing methods. Our code is available at
\url{https://github.com/xinli0928/NeFLAG}
