AI-Lancet: Locating Error-inducing Neurons to Optimize Neural Networks
Deep neural network (DNN) has been widely utilized in many areas due to its increasingly high accuracy. However, DNN models could also produce wrong outputs due to internal errors, which may lead to severe security issues. Unlike fixing bugs in traditional computer software, tracing the errors in DNN models and fixing them are much more difficult due to the uninterpretability of DNN. In this paper, we present a novel and systematic approach to trace and fix the errors in deep learning models. In particular, we locate the error-inducing neurons that play a leading role in the erroneous output. With the knowledge of error-inducing neurons, we propose two methods to fix the errors: the neuron-flip and the neuron-fine-tuning. We evaluate our approach using five different training datasets and seven different model architectures. The experimental results demonstrate its efficacy in different application scenarios, including backdoor removal and general defects fixing.
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