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: Capsule Networks have shown encouraging results on defacto benchmark computer vision datasets such as MNIST, CIFAR and smallNORB. Although, they are yet to be tested on tasks where (1) the entities detected inherently have more complex internal representations and (2) there are very few instances per class to learn from and (3) where point-wise classification is not suitable. Hence, this paper carries out experiments on face verification in both controlled and uncontrolled settings that together address these points. In doing so we introduce Siamese Capsule Networks, a new variant that can be used for pairwise learning tasks. The model is trained using contrastive loss with l2-normalized capsule encoded pose features. We find that Siamese Capsule Networks perform well against strong baselines on both pairwise learning datasets, yielding best results in the few-shot learning setting where image pairs in the test set contain unseen subjects.

Output: A variant of capsule networks that can be used for pairwise learning tasks. Results shows that Siamese Capsule Networks work well in the few shot learning setting.


Input: Consider Input: In the field of Generative Adversarial Networks (GANs), how to design a stable training strategy remains an open problem. Wasserstein GANs have largely promoted the stability over the original GANs by introducing Wasserstein distance, but still remain unstable and are prone to a variety of failure modes. In this paper, we present a general framework named Wasserstein-Bounded GAN (WBGAN), which improves a large family of WGAN-based approaches by simply adding an upper-bound constraint to the Wasserstein term. Furthermore, we show that WBGAN can reasonably measure the difference of distributions which almost have no intersection. Experiments demonstrate that WBGAN can stabilize as well as accelerate convergence in the training processes of a series of WGAN-based variants.

Output: Propose an improved framework for WGANs and demonstrate its better performance in theory and practice.


Input: Consider Input: Fine-grained Entity Recognition (FgER) is the task of detecting and classifying entity mentions to a large set of types spanning diverse domains such as biomedical, finance and sports.    We  observe  that  when  the  type  set  spans  several  domains,  detection  of  entity mention becomes a limitation for supervised learning models.   The primary reason being lack  of  dataset  where  entity  boundaries  are  properly  annotated  while  covering  a  large spectrum of entity types.   Our work directly addresses this issue.   We propose Heuristics Allied with Distant Supervision (HAnDS) framework to automatically construct a quality dataset suitable for the FgER task.   HAnDS framework exploits the high interlink among Wikipedia  and  Freebase  in  a  pipelined  manner,  reducing  annotation  errors  introduced by naively using distant supervision approach.   Using HAnDS framework,  we create two datasets, one suitable for building FgER systems recognizing up to 118 entity types based on the FIGER type hierarchy and another for up to 1115 entity types based on the TypeNet hierarchy.   Our extensive empirical experimentation warrants the quality of the generated datasets.   Along with this, we also provide a manually annotated dataset for benchmarking FgER systems.


Output: We initiate a push towards building ER systems to recognize thousands of types by providing a method to automatically construct suitable datasets based on the type hierarchy.