Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

Determining the optimal order in which data examples are presented to Deep Neural Networks during training is a non-trivial problem.
However, choosing a non-trivial scheduling method may drastically improve convergence.
In this paper, we propose a Self-Paced Learning (SPL)-fused Deep Metric Learning (DML) framework, which we call Learning Embeddings for Adaptive Pace (LEAP).
Our method parameterizes mini-batches dynamically based on the \textit{easiness} and \textit{true diverseness} of the sample within a salient feature representation space.
In LEAP, we train an \textit{embedding} Convolutional Neural Network (CNN) to learn an expressive representation space by adaptive density discrimination using the Magnet Loss.
The \textit{student} CNN classifier dynamically selects samples to form a mini-batch based on the \textit{easiness} from cross-entropy losses and \textit{true diverseness} of examples from the representation space sculpted by the \textit{embedding} CNN.
We evaluate LEAP using deep CNN architectures for the task of supervised image classification on MNIST, FashionMNIST, CIFAR-10, CIFAR-100, and SVHN.
We show that the LEAP framework converges faster with respect to the number of mini-batch updates required to achieve a comparable or better test performance on each of the datasets.
The standard method to train Deep Neural Networks (DNNs) is stochastic gradient descent (SGD) which employs backpropagation to compute gradients.
It typically relies on fixed-size mini-batches of random samples drawn from a finite dataset.
However, the contribution of each sample during model training varies across training iterations and configurations of the model's parameters BID15 .
This raises the importance of data scheduling for training DNNs, that is, searching for an optimal ordering of training examples which are presented to the model.
Previous studies on Curriculum Learning (Bengio et al., 2009, CL) show that organizing training samples based on the ascending order of difficulty can favour model training.
However, in CL, the curriculum remains fixed over the iterations and is determined without any knowledge or introspection of the model's learning.
Self-Paced Learning BID14 ) presents a method for dynamically generating a curriculum by biasing samples based on their easiness under the current model parameters.
This can lead to a highly imbalanced selection of samples, i.e. very few instances of some classes are chosen, which negatively affects the training process due to overfitting.
BID19 propose a simple batch selection strategy based on the loss values of training data for speeding up neural network training.
However, their results are limited and the approach is time-consuming, as it achieves high performance on MNIST, but fails on CIFAR-10.
Their work reveals that selecting the examples to present to a DNN is non-trivial, yet the strategy of uniformly sampling the training data set is not necessarily the optimal choice.
BID12 show that partitioning the data into groups with respect to diversity and easiness in their Self-Paced Learning with Diversity (SPLD) framework, can have substantial effect on training.
Rather than constraining the model to limited groups and areas, they propose to spread the sample selection as wide as possible to obtain diverse samples of similar easiness.
However, their use of K-Means and Spectral Clustering to partition the data into groups can lead to sub-optimal clustering results when learning non-linear feature representations.
Therefore, learning an appropriate metric by which to capture similarity among arbitrary groups of data is of great practical importance.
Deep Metric Learning (DML) approaches have recently attracted considerable attention and have been the focus of numerous studies BID1 ; BID23 ).
The most common methods are supervised, in which a feature space in which distance corresponds to class similarity is obtained.
The Magnet Loss BID21 presents state-of-the-art performance on fine-grained classification tasks.
BID26 show that it achieves state-of-the-art on clustering and retrieval tasks.
This paper makes two key contributions toward scheduling data examples in the mini-batch setting:• We propose a general sample selection framework called Learning Embeddings for Adap- tive Pace (LEAP) that is independent of model architecture or objective, and learns when to introduce certain samples to the DNN during training.•
To our knowledge, we are the first to leverage metric learning to improve self-paced learning. We
exploit a new type of knowledge -similar instance-level samples are discovered through an embedding network trained by DML in concert with the self-paced learner.2 RELEVANT
WORK
An important finding is that fusing a salient non-linear representation space with a dynamic learning strategy can help a DNN converge towards an optimal solution.
A random curriculum or a dynamic learning strategy without a good representation space was found to achieve a lower test accuracy or converge more slowly than LEAP.
Biasing samples based on the easiness and true diverseness to select mini-batches shows improvement in convergence to achieve classification performance comparable or better than the baselines, Random and SPLD.
As shown in TAB2 , the student CNN models show increased accuracy on MNIST, Fashion-MNIST, CIFAR-10, CIFAR-100 and SVHN with our LEAP sampling method.
It is to be noted that the LEAP framework improves the performance of complex convolutional architectures which already leverage regularization techniques such as batch normalization, dropout, and data augmentation.
We see that the improvements on coarse-grain datasets such as MNIST, Fashion-MNIST, CIFAR-10, and SVHN are between 0.11 and 0.81 percentage points.
On a fine-grained dataset like CIFAR-100, it is more challenging to obtain a high classification accuracy.
This is because there are a 100 fine-grained classes but the number of training instances for each class is small.
We have only 500 training images and 100 testing images per class.
In addition, the dataset contains images of low quality and images where only part of the object is visible (i.e. for a person, only head or only body).
However, we show that with LEAP, we can attain a significant increase in accuracy by 4.50 and 3.72 percentage points over the baselines SPLD and Random, respectively.
The mix of easy and diverse samples from a more accurate representation space of the data helps select appropriate samples during different stages of training and guide the network to achieve a higher classification accuracy, especially for more difficult fine-grained classifcation tasks.
Experimental results across all datasets (MNIST, Fashion-MNIST, CIFAR-10, CIFAR-100, and SVHN) and sampling methods (LEAP, SPLD, and Random).
The test accuracy (%) results are averaged over five runs, with the exception of CIFAR-100 and SVHN which had four runs each.
"*" indicates that no data augmentation scheme was applied on the dataset.In cases where the classification dataset is balanced and the classes are clearly identifiable, we showed that our end-to-end LEAP training protocol is practical.
An interesting line of work would be to apply LEAP on more complex real-world classification datasets such as iNaturalist BID8 , where there are imbalanced classes with a lot of diversity and require fine-grained visual recognition.
Another interesting area of application would be learning representations using DML for different computer vision tasks (e.g. human pose estimation, human activity recognition, semantic segmentation, etc.) and fusing a representative SPL strategy to train the student CNN.
We introduced LEAP, an end-to-end representation learning SPL strategy for adaptive mini-batch formation.
Our method uses an embedding CNN for learning an expressive representation space through a DML technique called the Magnet Loss.
The student CNN is a classifier which can exploit this new knowledge from the representation space to place the true diverseness and easiness as sample importance priors during online mini-batch selection.
The computational overhead of training two CNNs can be mitigated by training the embedding CNN and student CNN in parallel.
LEAP achieves good convergence speed and higher test performance on MNIST, FashionMNIST, CIFAR-10, CIFAR-100 and SVHN using a combination of two deep CNN architectures.
We hope this will help foster progress of end-to-end SPL fused DML strategies for DNN training, where a number of potentially interesting directions can be considered for further exploration.
Our framework is implemented in PyTorch and will be released as open-source on GitHub following the review process.
LEAP combines the strength of adaptive sampling with that of mini-batch online learning and adaptive representation learning to formulate a representative self-paced strategy in an end-to-end DNN training protocol.