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]: Designing a metric manually for unsupervised sequence generation tasks, such as text generation, is essentially difficult. In a such situation, learning a metric of a sequence from data is one possible solution. The previous study, SeqGAN, proposed the framework for unsupervised sequence generation, in which a metric is learned from data, and a generator is optimized with regard to the learned metric with policy gradient, inspired by generative adversarial nets (GANs) and reinforcement learning. In this paper, we make two proposals to learn better metric than SeqGAN's: partial reward function and expert-based reward function training. The partial reward function is a reward function for a partial sequence of a certain length. SeqGAN employs a reward function for completed sequence only. By combining long-scale and short-scale partial reward functions, we expect a learned metric to be able to evaluate a partial correctness as well as a coherence of a sequence, as a whole. In expert-based reward function training, a reward function is trained to discriminate between an expert (or true) sequence and a fake sequence that is produced by editing an expert sequence. Expert-based reward function training is not a kind of GAN frameworks. This makes the optimization of the generator easier. We examine the effect of the partial reward function and expert-based reward function training on synthetic data and real text data, and show improvements over SeqGAN and the model trained with MLE. Specifically, whereas SeqGAN gains 0.42 improvement of NLL over MLE on synthetic data, our best model gains 3.02 improvement, and whereas SeqGAN gains 0.029 improvement of BLEU over MLE, our best model gains 0.250 improvement.
[A]: This paper aims to learn a better metric for unsupervised learning, such as text generation, and shows a significant improvement over SeqGAN.


[Q]: Neural networks with low-precision weights and activations offer compelling
 efficiency advantages over their full-precision equivalents. The two most
 frequently discussed benefits of quantization are reduced memory consumption,
 and a faster forward pass when implemented with efficient bitwise
 operations. We propose a third benefit of very low-precision neural networks:
 improved robustness against some adversarial attacks, and in the worst case,
 performance that is on par with full-precision models. We focus on the very
 low-precision case where weights and activations are both quantized to $\pm$1,
 and note that stochastically quantizing weights in just one layer can sharply
 reduce the impact of iterative attacks. We observe that non-scaled binary neural
 networks exhibit a similar effect to the original \emph{defensive distillation}
 procedure that led to \emph{gradient masking}, and a false notion of security.
 We address this by conducting both black-box and white-box experiments with
 binary models that do not artificially mask gradients.
[A]: We conduct adversarial attacks against binarized neural networks and show that we reduce the impact of the strongest attacks, while maintaining comparable accuracy in a black-box setting


[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.