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.

Q: Targeted clean-label poisoning is a type of adversarial attack on machine learning systems where the adversary injects a few correctly-labeled, minimally-perturbed samples into the training data thus causing the deployed model to misclassify a particular test sample during inference. Although defenses have been proposed for general poisoning attacks (those which aim to reduce overall test accuracy), no reliable defense for clean-label attacks has been demonstrated, despite the attacks' effectiveness and their realistic use cases. In this work, we propose a set of simple, yet highly-effective defenses against these attacks. 
 We test our proposed approach against two recently published clean-label poisoning attacks, both of which use the CIFAR-10 dataset. After reproducing their experiments, we demonstrate that our defenses are able to detect over 99% of poisoning examples in both attacks and remove them without any compromise on model performance. Our simple defenses show that current clean-label poisoning attack strategies can be annulled, and serve as strong but simple-to-implement baseline defense for which to test future clean-label poisoning attacks.

A: We present effective defenses to clean-label poisoning attacks. 
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Q: The Convolutional Neural Network (CNN) has been successfully applied in many fields during recent decades; however it lacks the ability to utilize prior domain knowledge when dealing with many realistic problems. We present a framework called Geometric Operator Convolutional Neural Network (GO-CNN) that uses domain knowledge, wherein the kernel of the first convolutional layer is replaced with a kernel generated by a geometric operator function. This framework integrates many conventional geometric operators, which allows it to adapt to a diverse range of problems. Under certain conditions, we theoretically analyze the convergence and the bound of the generalization errors between GO-CNNs and common CNNs. Although the geometric operator convolution kernels have fewer trainable parameters than common convolution kernels, the experimental results indicate that GO-CNN performs more accurately than common CNN on CIFAR-10/100. Furthermore, GO-CNN reduces dependence on the amount of training examples and enhances adversarial stability.

A: Traditional image processing algorithms are combined with Convolutional Neural Networks，a new neural network.
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Q: A deep generative model is a powerful method of learning a data distribution, which has achieved tremendous success in numerous scenarios. However, it is nontrivial for a single generative model to faithfully capture the distributions of the complex data such as images with complicate structures. In this paper, we propose a novel approach of cascaded boosting for boosting generative models, where meta-models (i.e., weak learners) are cascaded together to produce a stronger model. Any hidden variable meta-model can be leveraged as long as it can support the likelihood evaluation. We derive a decomposable variational lower bound of the boosted model, which allows each meta-model to be trained separately and greedily. We can further improve the learning power of the generative models by combing our cascaded boosting framework with the multiplicative boosting framework.

A:
Propose an approach for boosting generative models by cascading hidden variable models
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