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We aim for image-based novelty detection. Despite considerable progress,existing models either fail or face a dramatic drop under the so-called"near-distribution" setting, where the differences between normal and anomaloussamples are subtle. We first demonstrate existing methods experience up to 20%decrease in performance in the near-distribution setting. Next, we propose toexploit a score-based generative model to produce synthetic near-distributionanomalous data. Our model is then fine-tuned to distinguish such data from thenormal samples. We provide a quantitative as well as qualitative evaluation ofthis strategy, and compare the results with a variety of GAN-based models.Effectiveness of our method for both the near-distribution and standard noveltydetection is assessed through extensive experiments on datasets in diverseapplications such as medical images, object classification, and qualitycontrol. This reveals that our method considerably improves over existingmodels, and consistently decreases the gap between the near-distribution andstandard novelty detection performance. The code repository is available athttps://github.com/rohban-lab/FITYMI.

Fake It Till You Make It: Towards Accurate Near-Distribution Novelty Detection