Figure 1: (a) Precision (blue) and recall (orange) for Figure 2: (a) Real data covers five modes (1-5) and several neighborhood sizes k. Both metrics were evaluated using 20k real and of varying sample count. Figure 1a illustrates the effect of varying k in the setup used in Figure 4b of the submission (truncation sweep 4 in StyleGAN, VGG-16 features, 50k samples). In general, different k yield consistent results and affect mainly the 5 saturation towards 0 or 1. Therefore, selecting k is a tradeoff between under-or overestimating the manifolds.

Calcium imaging is an important technique for monitoring the activity of thousands of neurons simultaneously. As calcium imaging datasets grow in size, automated detection of individual neurons is becoming important. Here we apply a supervised learning approach to this problem and show that convolutional networks can achieve near-human accuracy and superhuman speed. Accuracy is superior to the popular PCA/ICA method based on precision and recall relative to ground truth annotation by a human expert. These results suggest that convolutional networks are an efficient and flexible tool for the analysis of large-scale calcium imaging data.

Classical anomaly detection is principally concerned with point-based anomalies, those anomalies that occur at a single point in time. Yet, many real-world anomalies are range-based, meaning they occur over a period of time. Motivated by this observation, we present a new mathematical model to evaluate the accuracy of time series classification algorithms. Our model expands the well-known Precision and Recall metrics to measure ranges, while simultaneously enabling customization support for domain-specific preferences.

Recent advances in generative modeling have led to an increased interest in the study of statistical divergences as means of model comparison. Commonly used evaluation methods, such as the Fréchet Inception Distance (FID), correlate well with the perceived quality of samples and are sensitive to mode dropping. However, these metrics are unable to distinguish between different failure cases since they only yield one-dimensional scores. We propose a novel definition of precision and recall for distributions which disentangles the divergence into two separate dimensions. The proposed notion is intuitive, retains desirable properties, and naturally leads to an efficient algorithm that can be used to evaluate generative models. We relate this notion to total variation as well as to recent evaluation metrics such as Inception Score and FID. To demonstrate the practical utility of the proposed approach we perform an empirical study on several variants of Generative Adversarial Networks and Variational Autoencoders. In an extensive set of experiments we show that the proposed metric is able to disentangle the quality of generated samples from the coverage of the target distribution.

Neural Information Processing Systems http://nips.cc/

Akeytechnical step inourproofsisaparticular optimizationproblem oftheL2-Wasserstein distance over a constrained set of distributions.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/