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.

Input: Consider Input: We revisit the Recurrent Attention Model (RAM, Mnih et al. (2014)), a recurrent neural network for visual attention, from an active information sampling perspective. 

 We borrow ideas from neuroscience research on the role of active information sampling in the context of visual attention and gaze (Gottlieb, 2018), where the author suggested three types of motives for active information sampling strategies. We find the original RAM model only implements one of them.

 We identify three key weakness of the original RAM and provide a simple solution by adding two extra terms on the objective function. The modified RAM 1) achieves faster convergence, 2) allows dynamic decision making per sample without loss of accuracy, and 3) generalizes much better on longer sequence of glimpses which is not trained for, compared with the original RAM. 


Output:  Inspired by neuroscience research, solve three key weakness of the widely-cited recurrent attention model by simply adding two terms on the objective function.


Input: Consider Input: Generative Adversarial Networks (GANs), when trained on large datasets with diverse modes, are known to produce conflated images which do not distinctly belong to any of the modes. We hypothesize that this problem occurs due to the interaction between two facts: (1) For datasets with large variety, it is likely that the modes lie on separate manifolds. (2) The generator (G) is formulated as a continuous function, and the input noise is derived from a connected set, due to which G's output is a connected set. If G covers all modes, then there must be some portion of G's output which connects them. This corresponds to undesirable, conflated images. We develop theoretical arguments to support these intuitions. We propose a novel method to break the second assumption via learnable discontinuities in the latent noise space. Equivalently, it can be viewed as training several generators, thus creating discontinuities in the G function. We also augment the GAN formulation with a classifier C that predicts which noise partition/generator produced the output images, encouraging diversity between each partition/generator. We experiment on MNIST, celebA, STL-10, and a difficult dataset with clearly distinct modes, and show that the noise partitions correspond to different modes of the data distribution, and produce images of superior quality.

Output: We introduce theory to explain the failure of GANs on complex datasets and propose a solution to fix it.


Input: Consider Input: Generative priors have become highly effective in solving inverse problems including denoising, inpainting, and reconstruction from few and noisy measurements. With a generative model we can represent an image with a much lower dimensional latent codes. In the context of compressive sensing, if the unknown image belongs to the range of a pretrained generative network, then we can recover the image by estimating the underlying compact latent code from the available measurements. However, recent studies revealed that even untrained deep neural networks can work as a prior for recovering natural images. These approaches update the network weights keeping latent codes fixed to reconstruct the target image from the given measurements. In this paper, we optimize over network weights and latent codes to use untrained generative network as prior for video compressive sensing problem. We show that by optimizing over latent code, we can additionally get concise representation of the frames which retain the structural similarity of the video frames. We also apply low-rank constraint on the latent codes to represent the video sequences in even lower dimensional latent space. We empirically show that our proposed methods provide better or comparable accuracy and low computational complexity compared to the existing methods.


Output: Recover videos from compressive measurements by learning a low-dimensional (low-rank) representation directly from measurements while training a deep generator.