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 Deep Image Prior (DIP, Ulyanov et al., 2017) is a fascinating recent approach for recovering images which appear natural, yet is not fully understood. This work aims at shedding some further light on this approach by investigating the properties of the early outputs of the DIP. First, we show that these early iterations demonstrate invariance to adversarial perturbations by classifying progressive DIP outputs and using a novel saliency map approach. Next we explore using DIP as a defence against adversaries, showing good potential. Finally, we examine the adversarial invariancy of the early DIP outputs, and hypothesize that these outputs may remove non-robust image features. By comparing classification confidence values we show some evidence confirming this hypothesis.

Ex Output:
We investigate properties of the recently introduced Deep Image Prior (Ulyanov et al, 2017)


Ex Input:
We are reporting the SHINRA project, a project for structuring Wikipedia with collaborative construction scheme. The goal of the project is to create a huge and well-structured knowledge base to be used in NLP applications, such as QA, Dialogue systems and explainable NLP systems. It is created based on a scheme of ”Resource by Collaborative Contribution (RbCC)”. We conducted a shared task of structuring Wikipedia, and at the same, submitted results are used to construct a knowledge base.
 There are machine readable knowledge bases such as CYC, DBpedia, YAGO, Freebase Wikidata and so on, but each of them has problems to be solved. CYC has a coverage problem, and others have a coherence problem due to the fact that these are based on Wikipedia and/or created by many but inherently incoherent crowd workers. In order to solve the later problem, we started a project for structuring Wikipedia using automatic knowledge base construction shared-task.
 The automatic knowledge base construction shared-tasks have been popular and well studied for decades. However, these tasks are designed only to compare the performances of different systems, and to find which system ranks the best on limited test data. The results of the participated systems are not shared and the systems may be abandoned once the task is over.
 We believe this situation can be improved by the following changes:
 1. designing the shared-task to construct knowledge base rather than evaluating only limited test data
 2. making the outputs of all the systems open to public so that we can run ensemble learning to create the better results than the best systems
 3. repeating the task so that we can run the task with the larger and better training data from the output of the previous task (bootstrapping and active learning)
 We conducted “SHINRA2018” with the above mentioned scheme and in this paper
 we report the results and the future directions of the project. The task is to extract the values of the pre-defined attributes from Wikipedia pages. We have categorized most of the entities in Japanese Wikipedia (namely 730 thousand entities) into the 200 ENE categories. Based on this data, the shared-task is to extract the values of the attributes from Wikipedia pages. We gave out the 600 training data and the participants are required to submit the attribute-values for all remaining entities of the same category type. Then 100 data out of them for each category are used to evaluate the system output in the shared-task.
 We conducted a preliminary ensemble learning on the outputs and found 15 F1 score improvement on a category and the average of 8 F1 score improvements on all 5 categories we tested over a strong baseline. Based on this promising results, we decided to conduct three tasks in 2019; multi-lingual categorization task (ML), extraction for the same 5 categories in Japanese with a larger training data (JP-5) and extraction for 34 new categories in Japanese (JP-34).


Ex Output:
We introduce a "Resource by Collaborative Construction" scheme to create KB, structured Wikipedia 


Ex Input:
In a time where neural networks are increasingly adopted in sensitive applications, algorithmic bias has emerged as an issue with moral implications. While there are myriad ways that a system may be compromised by bias, systematically isolating and evaluating existing systems on such scenarios is non-trivial, i.e., bias may be subtle, natural and inherently difficult to quantify. To this end, this paper proposes the first systematic study of benchmarking state-of-the-art neural models against biased scenarios. More concretely, we postulate that the bias annotator problem can be approximated with neural models, i.e., we propose generative models of latent bias to deliberately and unfairly associate latent features to a specific class. All in all, our framework provides a new way for principled quantification and evaluation of models against biased datasets. Consequently, we find that state-of-the-art NLP models (e.g., BERT, RoBERTa, XLNET) are readily compromised by biased data.

Ex Output:

We propose a neural bias annotator to benchmark models on their robustness to biased text datasets.