46 research outputs found
Anomaly detection for imbalanced datasets with deep generative models
Many important data analysis applications present with severely imbalanced datasets with respect to the target variable. A typical example is medical image analysis, where positive samples are scarce, while performance is commonly estimated against the correct detection of these positive examples. We approach this challenge by formulating the problem as anomaly detection with generative models. We train a generative model without supervision on the ‘negative’ (common) datapoints and use this model to estimate the likelihood of unseen data. A successful model allows us to detect the ‘positive’ case as low likelihooddatapoints.In this position paper, we present the use of state-of-the-art deep generative models (GAN and VAE) for the estimation of a likelihood of the data. Our results show that on the one hand both GANs and VAEs are able to separate the ‘positive’ and ‘negative’ samples in the MNIST case. On the other hand, for the NLST case, neither GANs nor VAEs were able to capture the complexity of the data and discriminate anomalies at the level that this task requires. These results show that even though there are a number of successes presented in the literature for using generative models in similar applications, there remain further challenges for broad successful implementation
Uma atualização da lista de Cladocera Cladocera (Crustacea, Branchiopoda) do Estado de Pernambuco, Brasil
Physiological, isozyme changes and image analysis of popcorn seeds submitted to low temperatures
Efeitos da inclusão de torta de macaúba sobre a população de protozoários ruminais de caprinos
O estado atual do conhecimento da diversidade dos Cladocera (Crustacea, Branchiopoda) nas águas doces do estado de Minas Gerais
Zooplankton (Copepoda, Rotifera, Cladocera and Protozoa: Amoeba Testacea) from natural lakes of the middle Rio Doce basin, Minas Gerais, Brazil
Anomaly detection for imbalanced datasets with deep generative models
Many important data analysis applications present with severely imbalanced datasets with respect to the target variable. A typical example is medical image analysis, where positive samples are scarce, while performance is commonly estimated against the correct detection of these positive examples. We approach this challenge by formulating the problem as anomaly detection with generative models. We train a generative model without supervision on the ‘negative’ (common) datapoints and use this model to estimate the likelihood of unseen data. A successful model allows us to detect the ‘positive’ case as low likelihood datapoints. In this position paper, we present the use of state-of-the-art deep generative models (GAN and VAE) for the estimation of a likelihood of the data. Our results show that on the one hand both GANs and VAEs are able to separate the ‘positive’ and ‘negative’ samples in the MNIST case. On the other hand, for the NLST case, neither GANs nor VAEs were able to capture the complexity of the data and discriminate anomalies at the level that this task requires. These results show that even though there are a number of successes presented in the literature for using generative models in similar applications, there remain further challenges for broad successful implementation