user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A Neuro-AI Interface: Learning DNNs from the Human Brain

Paper content:
Deep neural networks (DNNs) are inspired from the human brain and the interconnection between the two has been widely studied in the literature.
However, it is still an open question whether DNNs are able to make decisions like the brain.
Previous work has demonstrated that DNNs, trained by matching the neural responses from inferior temporal (IT) cortex in monkey's brain, is able to achieve human-level performance on the image object recognition tasks.
This indicates that neural dynamics can provide informative knowledge to help DNNs accomplish specific tasks.
In this paper, we introduce the concept of a neuro-AI interface, which aims to use human's neural responses as supervised information for helping AI systems solve a task that is difficult when using traditional machine learning strategies.
In order to deliver the idea of neuro-AI interfaces, we focus on deploying it to one of the fundamental problems in generative adversarial networks (GANs): designing a proper evaluation metric to evaluate the quality of images produced by GANs.
Deep neural networks (DNNs) have successfully been applied to a number of different areas such as computer vision and natural language processing where they have demonstrated state-of-the-art results, often matching and even sometimes surpassing a human's ability.
Moreover, DNNs have been studied with respect to how similar processing is carried out in the human brain, where identifying these overlaps and interconnections has been a focus of study and investigation in the literature BID5 BID4 BID11 BID18 BID30 BID1 BID35 BID17 BID15 .
In this research area, convolutional neural networks (CNNs) are widely studied to be compared with the visual system in human's brain because of following reasons: (1) CNNs and human's visual system are both hierarchical system; (2) Steps of processing input between CNNs and human's visual system are similar to each other e.g., in a object recognition task, both CNNs and human recognize a object based on their its shape, edge, color etc..
Work BID35 outlines the use of CNNs approach for delving even more deeply into understanding the development and organization of sensory cortical processing.
It has been demonstrated that CNNs are able to reflect the spatio-temporal neural dynamics in human's brain visual area BID5 BID30 BID18 .
Despite lots of work is carried out to reveal the similarity between CNNs and brain system, research on interacting between CNNs and neural dynamics is less discussed in the literature as understanding of neural dynamics in the neuroscience area is still limited.There is a growing interest in studying generative adversarial networks (GANs) in the deep learning community BID10 .
Specifically, GANs have been widely applied to various domains such as computer vision BID14 , natural language processing BID7 and speech synthesis BID6 .
Compared with other deep generative models (e.g. variational autoencoders (VAEs)), GANs are favored for effectively handling sharp estimated density functions, efficiently generating desired samples and eliminating deterministic bias.
Due to these properties GANs have successfully contributed to plausible image generation BID14 , image to image translation BID38 , image super-resolution BID19 , image completion BID37 etc..
However, three main challenges still exist currently in the research of GANs: (1) Mode collapse -the model cannot learn the distribution of the full dataset well, which leads to poor generalization ability; (2) Difficult to trainit is non-trivial for discriminator and generator to achieve Nash equilibrium during the training; (3) Hard to evaluate -the evaluation of GANs can be considered as an effort to measure the dissimilarity between real distribution p r and generated distribution p g .
Unfortunately, the accurate estimation of p r is intractable.
Thus, it is challenging to have a good estimation of the correspondence between p r and p g .
Aspects (1) and (2) are more concerned with computational aspects where much research has been carried out to mitigate these issues BID20 Salimans et al., 2016; BID0 .
Aspect (3) is similarly fundamental, however, limited literature is available and most of the current metrics only focus on measuring the dissimilarity between training and generated images.
A more meaning-ful GANs evaluation metric that is consistent with human perceptions is paramount in helping researchers to further refine and design better GANs.Although some evaluation metrics, e.g., Inception Score (IS), Kernel Maximum Mean Discrepancy (MMD) and Fréchet Inception Distance (FID), have already been proposed (Salimans et al., 2016; BID13 BID2 , their limitations are obvious: (1) These metrics do not agree with human perceptual judgments and human rankings of GAN models.
A small artefact on images can have a large effect on the decision made by a machine learning system BID16 , whilst the intrinsic image content does not change.
In this aspect, we consider human perception to be more robust to adversarial images samples when compared to a machine learning system; (2) These metrics require large sample sizes for evaluation Salimans et al., 2016) .
Large-scale samples for evaluation sometimes are not realistic in real-world applications since it is time-consuming; and (3) They are not able to rank individual GAN-generated images by their quality i.e., the metrics are generated on a collection of images rather than on a single image basis.
The within GAN variances are crucial because it can provide the insight on the variability of that GAN.Work BID36 demonstrates that CNN matched with neural data recorded from inferior temporal cortex BID3 has high performance in object recognition tasks.
Given the evidence above that a CNN is able to predict the neural response in the brain, we describe a neuro-AI interface system, where human being's neural response is used as supervised information to help the AI system (CNNs used in this work) solve more difficult problems in real-world.
As a starting point for exploiting the idea of neuro-AI interface, we focus on utilizing it to solve one of the fundamental problems in GANs: designing a proper evaluation metric.
In this paper, we introduce a neuro-AI interface that interacts CNNs with neural signals.
We demonstrate the use of neuro-AI interface by introducing a challenge in the area of GANs i.e., evaluate the quality of images produced by GANs.
Three deep network architectures are explored and the results demonstrate that including neural responses during the training phase of the neuro-AI interface improves its accuracy even when neural measurements are absent when evaluating on the test set.
More details of the performance of Neuroscore can be referred in Appendix.
FIG1 shows the averaged reconstructed P300 signal across all participants (using LDA beamformer) in the RSVP experiment.
It should be noted here that the averaged reconstructed P300 signal is calculated as the difference between averaged target trials and averaged standard trials after applying the LDA beamformer method.
The solid lines in FIG1 are the means of the averaged reconstructed P300 signals for each image category (across 12 participants) while the shaded areas represent the standard deviations (across participants).
It can be seen that the averaged reconstructed P300 (across participants) clearly distinguishes between different image categories.
In order to statistically measure this correlative relationship, we calculated the Pearson correlation coefficient and p-value (two-tailed) between Neuroscore and BE accuracy and found (r(48) = −0.767, p = 2.089e − 10).
We also did the Pearson statistical test and bootstrap on the correlation between Neuroscore and BE accuracy (human judgment performance) only for GANs i.e., DCGAN, BEGAN and PROGAN.
Pearson statistic is (r(36)=-0.827, p=4.766e-10) and the bootstrapped p ≤ 0.0001.

assistant: text: Describe a neuro-AI interface technique to evaluate generative adversarial networks