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:   In many applications, the training data for a machine learning task is partitioned across multiple nodes, and aggregating this data may be infeasible due to storage, communication, or privacy constraints. In this work, we present Good-Enough Model Spaces (GEMS), a novel framework for learning a global satisficing (i.e. "good-enough") model within a few communication rounds by carefully combining the space of local nodes' satisficing models. In experiments on benchmark and medical datasets, our approach outperforms other baseline aggregation techniques such as ensembling or model averaging, and performs comparably to the ideal non-distributed models.


A: We present Good-Enough Model Spaces (GEMS), a framework for learning an aggregate model over distributed nodes within a small number of communication rounds.
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Q: Object recognition in real-world requires handling long-tailed or even open-ended data. An ideal visual system needs to reliably recognize the populated visual concepts and meanwhile efficiently learn about emerging new categories with a few training instances. Class-balanced many-shot learning and few-shot learning tackle one side of this problem, via either learning strong classifiers for populated categories or learning to learn few-shot classifiers for the tail classes. In this paper, we investigate the problem of generalized few-shot learning (GFSL) -- a model during the deployment is required to not only learn about "tail" categories with few shots, but simultaneously classify the "head" and "tail" categories. We propose the Classifier Synthesis Learning (CASTLE), a learning framework that learns how to synthesize calibrated few-shot classifiers in addition to the multi-class classifiers of ``head'' classes, leveraging a shared neural dictionary. CASTLE sheds light upon the inductive GFSL through optimizing one clean and effective GFSL learning objective. It demonstrates superior performances than existing GFSL algorithms and strong baselines on MiniImageNet and TieredImageNet data sets. More interestingly, it outperforms previous state-of-the-art methods when evaluated on standard few-shot learning.

A:
We propose to learn synthesizing few-shot classifiers and many-shot classifiers using one single objective function for GFSL.
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