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.

[Q]: Generative Adversarial Networks (GANs), when trained on large datasets with diverse modes, are known to produce conflated images which do not distinctly belong to any of the modes. We hypothesize that this problem occurs due to the interaction between two facts: (1) For datasets with large variety, it is likely that the modes lie on separate manifolds. (2) The generator (G) is formulated as a continuous function, and the input noise is derived from a connected set, due to which G's output is a connected set. If G covers all modes, then there must be some portion of G's output which connects them. This corresponds to undesirable, conflated images. We develop theoretical arguments to support these intuitions. We propose a novel method to break the second assumption via learnable discontinuities in the latent noise space. Equivalently, it can be viewed as training several generators, thus creating discontinuities in the G function. We also augment the GAN formulation with a classifier C that predicts which noise partition/generator produced the output images, encouraging diversity between each partition/generator. We experiment on MNIST, celebA, STL-10, and a difficult dataset with clearly distinct modes, and show that the noise partitions correspond to different modes of the data distribution, and produce images of superior quality.
[A]: We introduce theory to explain the failure of GANs on complex datasets and propose a solution to fix it.


[Q]: For natural language understanding (NLU) technology to be maximally useful, it must be able to process language in a way that is not exclusive to a single task, genre, or dataset. In pursuit of this objective, we introduce the General Language Understanding Evaluation (GLUE) benchmark, a collection of tools for evaluating the performance of models across a diverse set of existing NLU tasks. By including tasks with limited training data, GLUE is designed to favor and encourage models that share general linguistic knowledge across tasks. GLUE also includes a hand-crafted diagnostic test suite that enables detailed linguistic analysis of models. We evaluate baselines based on current methods for transfer and representation learning and find that multi-task training on all tasks performs better than training a separate model per task. However, the low absolute performance of our best model indicates the need for improved general NLU systems.
[A]: We present a multi-task benchmark and analysis platform for evaluating generalization in natural language understanding systems.


[Q]: We seek to auto-generate stronger  input features  for ML methods faced with limited training data.
 Biological neural nets (BNNs) excel at fast learning, implying that they extract highly informative features. In particular, the insect olfactory network  learns new odors very rapidly, by means of three key elements: A competitive inhibition layer; randomized, sparse connectivity into a high-dimensional sparse plastic layer; and Hebbian updates of synaptic weights. In this work we deploy MothNet, a computational model of the moth olfactory network, as an automatic feature generator. Attached as a front-end pre-processor, MothNet's readout neurons provide new features, derived from the original features, for use by standard ML classifiers. These ``insect cyborgs'' (part BNN and part ML method) have significantly better performance than baseline ML methods alone on vectorized MNIST and Omniglot data sets, reducing test set error averages 20% to 55%. The MothNet feature generator also substantially out-performs other feature generating methods including PCA, PLS, and NNs. These results highlight the potential value of BNN-inspired feature generators in the ML context.
[A]:
Features auto-generated by the bio-mimetic MothNet model significantly improve the test accuracy of standard ML methods on vectorized MNIST. The MothNet-generated features also outperform standard feature generators.