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]: Most recent gains in visual recognition have originated from the inclusion of attention mechanisms in deep convolutional networks (DCNs). Because these networks are optimized for object recognition, they learn where to attend using only a weak form of supervision derived from image class labels. Here, we demonstrate the benefit of using stronger supervisory signals by teaching DCNs to attend to image regions that humans deem important for object recognition. We first describe a large-scale online experiment (ClickMe) used to supplement ImageNet with nearly half a million human-derived "top-down" attention maps. Using human psychophysics, we confirm that the identified top-down features from ClickMe are more diagnostic than "bottom-up" saliency features for rapid image categorization. As a proof of concept, we extend a state-of-the-art attention network and demonstrate that adding ClickMe supervision significantly improves its accuracy and yields visual features that are more interpretable and more similar to those used by human observers.
[A]: A large-scale dataset for training attention models for object recognition leads to more accurate, interpretable, and human-like object recognition.

[Q]: Deep neural networks provide state-of-the-art performance for many applications of interest. Unfortunately they are known to be vulnerable to adversarial examples, formed by applying small but malicious perturbations to the original inputs. Moreover, the perturbations can transfer across models: adversarial examples generated for a specific model will often mislead other unseen models. Consequently the adversary can leverage it to attack against the deployed black-box systems.
In this work, we demonstrate that the adversarial perturbation can be decomposed into two components: model-specific and data-dependent one, and it is the latter that mainly contributes to the transferability. Motivated by this understanding, we propose to craft adversarial examples by utilizing the noise reduced gradient (NRG) which approximates the data-dependent component. Experiments on various classification models trained on ImageNet demonstrates that the new approach enhances the transferability dramatically. We also find that low-capacity models have more powerful attack capability than high-capacity counterparts, under the condition that they have comparable test performance. These insights give rise to a principled manner to construct adversarial examples with high success rates and could potentially provide us guidance for designing effective defense approaches against black-box attacks.
[A]: We propose a new method for enhancing the transferability of adversarial examples by using the noise-reduced gradient.

[Q]: Medical images may contain various types of artifacts with different patterns and mixtures, which depend on many factors such as scan setting, machine condition, patients' characteristics, surrounding environment, etc. However, existing deep learning based artifact reduction methods are restricted by their training set with specific predetermined artifact type and pattern. As such, they have limited clinical adoption. In this paper, we introduce a "Zero-Shot" medical image Artifact Reduction (ZSAR) framework, which leverages the power of deep learning but without using general pre-trained networks or any clean image reference. Specifically, we utilize the low internal visual entropy of an image and train a light-weight image-specific artifact reduction network to reduce artifacts in an image at test-time. We use Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) as vehicles to show that ZSAR can reduce artifacts better than state-of-the-art both qualitatively and quantitatively, while using shorter execution time. To the best of our knowledge, this is the first deep learning framework that reduces artifacts in medical images without using a priori training set.
[A]:
We introduce a "Zero-Shot" medical image Artifact Reduction framework, which leverages the power of deep learning but without using general pre-trained networks or any clean image reference.