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 Q]: We propose a framework for extreme learned image compression based on Generative Adversarial Networks (GANs), obtaining visually pleasing images at significantly lower bitrates than previous methods. This is made possible through our GAN formulation of learned compression combined with a generator/decoder which operates on the full-resolution image and is trained in combination with a multi-scale discriminator. Additionally, if a semantic label map of the original image is available, our method can fully synthesize unimportant regions in the decoded image such as streets and trees from the label map, therefore only requiring the storage of the preserved region and the semantic label map. A user study confirms that for low bitrates, our approach is preferred to state-of-the-art methods, even when they use more than double the bits.
[EX A]: GAN-based extreme image compression method using less than half the bits of the SOTA engineered codec while preserving visual quality

[EX Q]: In this work, we attempt to answer a critical question: whether there exists some input sequence that will cause a well-trained discrete-space neural network sequence-to-sequence (seq2seq) model to generate egregious outputs (aggressive, malicious, attacking, etc.). And if such inputs exist, how to find them efficiently. We adopt an empirical methodology, in which we first create lists of egregious output sequences, and then design a discrete optimization algorithm to find input sequences that will cause the model to generate them. Moreover, the optimization algorithm is enhanced for large vocabulary search and constrained to search for input sequences that are likely to be input by real-world users. In our experiments, we apply this approach to dialogue response generation models trained on three real-world dialogue data-sets: Ubuntu, Switchboard and OpenSubtitles, testing whether the model can generate malicious responses. We demonstrate that given the trigger inputs our algorithm finds, a significant number of malicious sentences are assigned large probability by the model, which reveals an undesirable consequence of standard seq2seq training.
[EX A]: This paper aims to provide an empirical answer to the question of whether well-trained dialogue response model can output malicious responses.

[EX Q]: We consider the problem of weakly supervised structured prediction (SP) with reinforcement learning (RL) for example, given a database table and a question, perform a sequence of computation actions on the table, which generates a response and receives a binary success-failure reward. This line of research has been successful by leveraging RL to directly optimizes the desired metrics of the SP tasks for example, the accuracy in question answering or BLEU score in machine translation. However, different from the common RL settings, the environment dynamics is deterministic in SP, which hasn't been fully utilized by the model-freeRL methods that are usually applied. Since SP models usually have full access to the environment dynamics, we propose to apply model-based RL methods, which rely on planning as a primary model component. We demonstrate the effectiveness of planning-based SP with a Neural Program Planner (NPP), which, given a set of candidate programs from a pretrained search policy, decides which program is the most promising considering all the information generated from executing these programs. We evaluate NPP on weakly supervised program synthesis from natural language(semantic parsing) by stacked learning a planning module based on pretrained search policies. On the WIKITABLEQUESTIONS benchmark, NPP achieves a new state-of-the-art of 47.2% accuracy.
[EX A]:
A model-based planning component improves RL-based semantic parsing on WikiTableQuestions.