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In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

Convolutional neural networks memorize part of their training data, which is why strategies such as data augmentation and drop-out are employed to mitigate over- fitting. This paper considers the related question of "membership inference", where the goal is to determine if an image was used during training. We con- sider membership tests over either ensembles of samples or individual samples.
First, we show how to detect if a dataset was used to train a model, and in particular whether some validation images were used at train time. Then, we introduce a new approach to infer membership when a few of the top layers are not available or have been fine-tuned, and show that lower layers still carry information about the training samples. To support our findings, we conduct large-scale experiments on Imagenet and subsets of YFCC-100M with modern architectures such as VGG and Resnet.

Output:
We analyze the memorization properties by a convnet of the training set and propose several use-cases where we can extract some information about the training set.