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.

Limited angle CT reconstruction is an under-determined linear inverse problem that requires appropriate regularization techniques to be solved. In this work we study how pre-trained generative adversarial networks (GANs) can be used to clean noisy, highly artifact laden reconstructions from conventional techniques, by effectively projecting onto the inferred image manifold. In particular, we use a robust version of the popularly used GAN prior for inverse problems, based on a recent technique called corruption mimicking, that significantly improves the reconstruction quality. The proposed approach operates in the image space directly, as a result of which it does not need to be trained or require access to the measurement model, is scanner agnostic, and can work over a wide range of sensing scenarios.
We show that robust GAN priors work better than GAN priors for limited angle CT reconstruction which is a highly under-determined inverse problem.

Flow-based generative models are powerful exact likelihood models with efficient sampling and inference. 
 Despite their computational efficiency, flow-based models generally have much worse density modeling performance compared to state-of-the-art autoregressive models. In this paper, we investigate and improve upon three limiting design choices employed by flow-based models in prior work: the use of uniform noise for dequantization, the use of inexpressive affine flows, and the use of purely convolutional conditioning networks in coupling layers. Based on our findings, we propose Flow++, a new flow-based model that is now the state-of-the-art non-autoregressive model for unconditional density estimation on standard image benchmarks. Our work has begun to close the significant performance gap that has so far existed between autoregressive models and flow-based models.
Improved training of current flow-based generative models (Glow and RealNVP) on density estimation benchmarks

Emotion is playing a great role in our daily lives. The necessity and importance of an automatic Emotion recognition system is getting increased. Traditional approaches of emotion recognition are based on facial images, measurements of heart rates, blood pressure, temperatures, tones of voice/speech, etc. However, these features can potentially be changed to fake features. So to detect hidden and real features that is not controlled by the person are data measured from brain signals. There are various ways of measuring brain waves: EEG, MEG, FMRI, etc. On the bases of cost effectiveness and performance trade-offs, EEG is chosen for emotion recognition in this work. The main aim of this study is to detect emotion based on EEG signal analysis recorded from brain in response to visual stimuli. The approaches used were the selected visual stimuli were presented to 11 healthy target subjects and EEG signal were recorded in controlled situation to minimize artefacts (muscle or/and eye movements).   The signals were filtered and type of frequency band was computed and detected. The proposed method predicts an emotion type (positive/negative) in response to the presented stimuli. Finally, the performance of the proposed approach was tested. The average accuracy of machine learning algorithms (i.e. J48, Bayes Net, Adaboost and Random Forest) are 78.86, 74.76, 77.82 and 82.46 respectively.   In this study, we also applied EEG applications in the context of neuro-marketing. The results empirically demonstrated detection of the favourite colour preference of customers in response to the logo colour of an organization or Service.

This paper presents EEG based emotion detection of a person towards an image stimuli and its applicability on neuromarketing.