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.

Ex Input:
The application of deep recurrent networks to audio transcription has led to impressive gains in automatic speech recognition (ASR) systems. Many have demonstrated that small adversarial perturbations can fool deep neural networks into incorrectly predicting a specified target with high confidence. Current work on fooling ASR systems have focused on white-box attacks, in which the model architecture and parameters are known. In this paper, we adopt a black-box approach to adversarial generation, combining the approaches of both genetic algorithms and gradient estimation to solve the task. We achieve a 89.25% targeted attack similarity after 3000 generations while maintaining 94.6% audio file similarity.

Ex Output:
We present a novel black-box targeted attack that is able to fool state of the art speech to text transcription.


Ex Input:
Deep networks often perform well on the data distribution on which they are trained, yet give incorrect (and often very confident) answers when evaluated on points from off of the training distribution. This is exemplified by the adversarial examples phenomenon but can also be seen in terms of model generalization and domain shift.   Ideally, a model would assign lower confidence to points unlike those from the training distribution.   We propose a regularizer which addresses this issue by training with interpolated hidden states and encouraging the classifier to be less confident at these points.   Because the hidden states are learned, this has an important effect of encouraging the hidden states for a class to be concentrated in such a way so that interpolations within the same class or between two different classes do not intersect with the real data points from other classes.   This has a major advantage in that it avoids the underfitting which can result from interpolating in the input space.   We prove that the exact condition for this problem of underfitting to be avoided by Manifold Mixup is that the dimensionality of the hidden states exceeds the number of classes, which is often the case in practice.   Additionally, this concentration can be seen as making the features in earlier layers more discriminative.   We show that despite requiring no significant additional computation, Manifold Mixup achieves large improvements over strong baselines in supervised learning, robustness to single-step adversarial attacks, semi-supervised learning, and Negative Log-Likelihood on held out samples.

Ex Output:
A method for learning better representations, that acts as a regularizer and despite its no significant additional computation cost , achieves improvements over strong baselines on Supervised and Semi-supervised Learning tasks.


Ex Input:
This paper addresses unsupervised few-shot object recognition, where all training images are unlabeled and do not share classes with labeled support images for few-shot recognition in testing. We use a new GAN-like deep architecture aimed at unsupervised learning of an image representation which will encode latent object parts and thus generalize well to unseen classes in our few-shot recognition task. Our unsupervised training integrates adversarial, self-supervision, and deep metric learning. We make two contributions. First, we extend the vanilla GAN with reconstruction loss to enforce the discriminator capture the most relevant characteristics of "fake" images generated from randomly sampled codes. Second, we compile a training set of triplet image examples for estimating the triplet loss in metric learning by using an image masking procedure suitably designed to identify latent object parts. Hence, metric learning ensures that the deep representation of images showing similar object classes which share some parts are closer than the representations of images which do not have common parts. Our results show that we significantly outperform the state of the art, as well as get similar performance to the common episodic training for fully-supervised few-shot learning on the Mini-Imagenet and Tiered-Imagenet datasets.

Ex Output:

We address the problem of unsupervised few-shot object recognition, where all training images are unlabeled and do not share classes with test images.