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.

Model distillation aims to distill the knowledge of a complex model into a simpler one. In this paper, we consider an alternative formulation called dataset distillation: we keep the model fixed and instead attempt to distill the knowledge from a large training dataset into a small one. The idea is to synthesize a small number of data points that do not need to come from the correct data distribution, but will, when given to the learning algorithm as training data, approximate the model trained on the original data. For example, we show that it is possible to compress 60,000 MNIST training images into just 10 synthetic distilled images (one per class) and achieve close to the original performance, given a fixed network initialization. We evaluate our method in various initialization settings.   Experiments on multiple datasets, MNIST, CIFAR10, PASCAL-VOC, and CUB-200, demonstrate the ad-vantage of our approach compared to alternative methods.   Finally, we include a real-world application of dataset distillation to the continual learning setting: we show that storing distilled images as episodic memory of previous tasks can alleviate forgetting more effectively than real images.
We propose to distill a large dataset into a small set of synthetic data that can train networks close to original performance. 

We extend the Consensus Network framework to Transductive Consensus Network (TCN), a semi-supervised multi-modal classification framework, and identify its two mechanisms: consensus and classification. By putting forward three variants as ablation studies, we show both mechanisms should be functioning together. Overall, TCNs outperform or align with the best benchmark algorithms when only 20 to 200 labeled data points are available.
A semi-supervised multi-modal classification framework, TCN, that outperforms various benchmarks.

Automatic melody generation for pop music has been a long-time aspiration for
 both AI researchers and musicians. However, learning to generate euphonious
 melody has turned out to be highly challenging due to a number of factors. Representation
 of multivariate property of notes has been one of the primary challenges.
 It is also difficult to remain in the permissible spectrum of musical variety, outside
 of which would be perceived as a plain random play without auditory pleasantness.
 Observing the conventional structure of pop music poses further challenges.
 In this paper, we propose to represent each note and its properties as a unique
 ‘word,’ thus lessening the prospect of misalignments between the properties, as
 well as reducing the complexity of learning. We also enforce regularization policies
 on the range of notes, thus encouraging the generated melody to stay close
 to what humans would find easy to follow. Furthermore, we generate melody
 conditioned on song part information, thus replicating the overall structure of a
 full song. Experimental results demonstrate that our model can generate auditorily
 pleasant songs that are more indistinguishable from human-written ones than
 previous models.

We propose a novel model to represent notes and their properties, which can enhance the automatic melody generation.