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One way to interpret trained deep neural networks (DNNs) is by inspecting characteristics that neurons in the model respond to, such as by iteratively optimising themodelinput(e.g.,animage)tomaximallyactivatespecificneurons. However, this requires a careful selection of hyper-parameters to generate interpretable examples for each neuron of interest, and current methods rely on a manual, qualitative evaluation of each setting, which is prohibitively slow. We introduce a new metricthatusesFr´echetInceptionDistance(FID)toencouragesimilaritybetween model activations for real and generated data. This provides an efficient way to evaluateasetofgeneratedexamplesforeachsettingofhyper-parameters. Wealso propose a novel GAN-based method for generating explanations that enables an efficient search through the input space and imposes a strong prior favouring realistic outputs. We apply our approach to a classification model trained to predict whether a music audio recording contains singing voice. Our results suggest that thisproposedmetricsuccessfullyselectshyper-parametersleadingtointerpretable examples, avoiding the need for manual evaluation. Moreover, we see that examples synthesised to maximise or minimise the predicted probability of singing voice presence exhibit vocal or non-vocal characteristics, respectively, suggesting that our approach is able to generate suitable explanations for understanding concepts learned by a neural network., QC 20190925

One way to interpret trained deep neural networks (DNNs) is by inspecting characteristics that neurons in the model respond to, such as by iteratively optimising themodelinput(e.g.,animage)tomaximallyactivatespecificneurons. However, this requires a careful selection of hyper-parameters to generate interpretable examples for each neuron of interest, and current methods rely on a manual, qualitative evaluation of each setting, which is prohibitively slow. We introduce a new metricthatusesFr´echetInceptionDistance(FID)toencouragesimilaritybetween model activations for real and generated data. This provides an efficient way to evaluateasetofgeneratedexamplesforeachsettingofhyper-parameters. Wealso propose a novel GAN-based method for generating explanations that enables an efficient search through the input space and imposes a strong prior favouring realistic outputs. We apply our approach to a classification model trained to predict whether a music audio recording contains singing voice. Our results suggest that thisproposedmetricsuccessfullyselectshyper-parametersleadingtointerpretable examples, avoiding the need for manual evaluation. Moreover, we see that examples synthesised to maximise or minimise the predicted probability of singing voice presence exhibit vocal or non-vocal characteristics, respectively, suggesting that our approach is able to generate suitable explanations for understanding concepts learned by a neural network., QC 20190925

One way to interpret trained deep neural networks (DNNs) is by inspecting characteristics that neurons in the model respond to, such as by iteratively optimising themodelinput(e.g.,animage)tomaximallyactivatespecificneurons. However, this requires a careful selection of hyper-parameters to generate interpretable examples for each neuron of interest, and current methods rely on a manual, qualitative evaluation of each setting, which is prohibitively slow. We introduce a new metricthatusesFr´echetInceptionDistance(FID)toencouragesimilaritybetween model activations for real and generated data. This provides an efficient way to evaluateasetofgeneratedexamplesforeachsettingofhyper-parameters. Wealso propose a novel GAN-based method for generating explanations that enables an efficient search through the input space and imposes a strong prior favouring realistic outputs. We apply our approach to a classification model trained to predict whether a music audio recording contains singing voice. Our results suggest that thisproposedmetricsuccessfullyselectshyper-parametersleadingtointerpretable examples, avoiding the need for manual evaluation. Moreover, we see that examples synthesised to maximise or minimise the predicted probability of singing voice presence exhibit vocal or non-vocal characteristics, respectively, suggesting that our approach is able to generate suitable explanations for understanding concepts learned by a neural network., QC 20190925

One way to interpret trained deep neural networks (DNNs) is by inspecting characteristics that neurons in the model respond to, such as by iteratively optimising themodelinput(e.g.,animage)tomaximallyactivatespecificneurons. However, this requires a careful selection of hyper-parameters to generate interpretable examples for each neuron of interest, and current methods rely on a manual, qualitative evaluation of each setting, which is prohibitively slow. We introduce a new metricthatusesFr´echetInceptionDistance(FID)toencouragesimilaritybetween model activations for real and generated data. This provides an efficient way to evaluateasetofgeneratedexamplesforeachsettingofhyper-parameters. Wealso propose a novel GAN-based method for generating explanations that enables an efficient search through the input space and imposes a strong prior favouring realistic outputs. We apply our approach to a classification model trained to predict whether a music audio recording contains singing voice. Our results suggest that thisproposedmetricsuccessfullyselectshyper-parametersleadingtointerpretable examples, avoiding the need for manual evaluation. Moreover, we see that examples synthesised to maximise or minimise the predicted probability of singing voice presence exhibit vocal or non-vocal characteristics, respectively, suggesting that our approach is able to generate suitable explanations for understanding concepts learned by a neural network., QC 20190925

One way to interpret trained deep neural networks (DNNs) is by inspecting characteristics that neurons in the model respond to, such as by iteratively optimising themodelinput(e.g.,animage)tomaximallyactivatespecificneurons. However, this requires a careful selection of hyper-parameters to generate interpretable examples for each neuron of interest, and current methods rely on a manual, qualitative evaluation of each setting, which is prohibitively slow. We introduce a new metricthatusesFr´echetInceptionDistance(FID)toencouragesimilaritybetween model activations for real and generated data. This provides an efficient way to evaluateasetofgeneratedexamplesforeachsettingofhyper-parameters. Wealso propose a novel GAN-based method for generating explanations that enables an efficient search through the input space and imposes a strong prior favouring realistic outputs. We apply our approach to a classification model trained to predict whether a music audio recording contains singing voice. Our results suggest that thisproposedmetricsuccessfullyselectshyper-parametersleadingtointerpretable examples, avoiding the need for manual evaluation. Moreover, we see that examples synthesised to maximise or minimise the predicted probability of singing voice presence exhibit vocal or non-vocal characteristics, respectively, suggesting that our approach is able to generate suitable explanations for understanding concepts learned by a neural network., QC 20190925