Query: "Integrating digital citizenship" "Curriculum"

title: Teacher Student Curriculum Learning
abstract: We propose Teacher Student Curriculum Learning (TSCL) a framework for automatic curriculum learning, where the Student tries to learn a complex task, and the Teacher automatically chooses subtasks from a given set for the Student to train on. We describe a family of Teacher algorithms that rely on the intuition that the Student should practice more those tasks on which it makes the fastest progress, i.e. where the slope of the learning curve is highest. In addition, the Teacher algorithms address the problem of forgetting by also choosing tasks where the Student's performance is getting worse. We demonstrate that TSCL matches or surpasses the results of carefully hand crafted curricula in two tasks: addition of decimal numbers with long short term memory (LSTM) and navigation in Minecraft. Our automatically ordered curriculum of submazes enabled to solve a Minecraft maze that could not be solved at all when training directly on that maze, and the learning was an order of magnitude faster than a uniform sampling of those submazes.

title: The impact of COVID 19 on the undergraduate medical curriculum
abstract: The coronavirus pandemic has impacted medical education globally. As universities seek to deliver medical education through new methods of modalities, this continuing of education ensures the learning of the future workforce of the NHS. Novel ways of online teaching should be considered in new medical curricula development, as well as methods of delivering practical skills for medical students online.

title: A Curriculum Domain Adaptation Approach to the Semantic Segmentation of Urban Scenes
abstract: During the last half decade, convolutional neural networks (CNNs) have triumphed over semantic segmentation, which is one of the core tasks in many applications such as autonomous driving and augmented reality. However, to train CNNs requires a considerable amount of data, which is difficult to collect and laborious to annotate. Recent advances in computer graphics make it possible to train CNNs on photo realistic synthetic imagery with computer generated annotations. Despite this, the domain mismatch between real images and the synthetic data hinders the models' performance. Hence, we propose a curriculum style learning approach to minimizing the domain gap in urban scene semantic segmentation. The curriculum domain adaptation solves easy tasks first to infer necessary properties about the target domain; in particular, the first task is to learn global label distributions over images and local distributions over landmark superpixels. These are easy to estimate because images of urban scenes have strong idiosyncrasies (e.g. the size and spatial relations of buildings, streets, cars, etc. We then train a segmentation network, while regularizing its predictions in the target domain to follow those inferred properties. In experiments, our method outperforms the baselines on two datasets and three backbone networks. We also report extensive ablation studies about our approach.

title: Curriculum Loss: Robust Learning and Generalization against Label Corruption
abstract: Deep neural networks (DNNs) have great expressive power, which can even memorize samples with wrong labels. It is vitally important to reiterate robustness and generalization in DNNs against label corruption. To this end, this paper studies the 0 1 loss, which has a monotonic relationship with an empirical adversary (reweighted) risk~\citep{hu2016does} Although the 0 1 loss has some robust properties, it is difficult to optimize. To efficiently optimize the 0 1 loss while keeping its robust properties, we propose a very simple and efficient loss, i.e. curriculum loss (CL) Our CL is a tighter upper bound of the 0 1 loss compared with conventional summation based surrogate losses. Moreover, CL can adaptively select samples for model training. As a result, our loss can be deemed as a novel perspective of curriculum sample selection strategy, which bridges a connection between curriculum learning and robust learning. Experimental results on benchmark datasets validate the robustness of the proposed loss.

title: CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition
abstract: As an emerging topic in face recognition, designing margin based loss functions can increase the feature margin between different classes for enhanced discriminability. More recently, the idea of mining based strategies is adopted to emphasize the misclassified samples, achieving promising results. However, during the entire training process, the prior methods either do not explicitly emphasize the sample based on its importance that renders the hard samples not fully exploited; or explicitly emphasize the effects of semi hard/hard samples even at the early training stage that may lead to convergence issue. In this work, we propose a novel Adaptive Curriculum Learning loss (CurricularFace) that embeds the idea of curriculum learning into the loss function to achieve a novel training strategy for deep face recognition, which mainly addresses easy samples in the early training stage and hard ones in the later stage. Specifically, our CurricularFace adaptively adjusts the relative importance of easy and hard samples during different training stages. In each stage, different samples are assigned with different importance according to their corresponding difficultness. Extensive experimental results on popular benchmarks demonstrate the superiority of our CurricularFace over the state of the art competitors.

title: Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey
abstract: Reinforcement learning (RL) is a popular paradigm for addressing sequential decision tasks in which the agent has only limited environmental feedback. Despite many advances over the past three decades, learning in many domains still requires a large amount of interaction with the environment, which can be prohibitively expensive in realistic scenarios. To address this problem, transfer learning has been applied to reinforcement learning such that experience gained in one task can be leveraged when starting to learn the next, harder task. More recently, several lines of research have explored how tasks, or data samples themselves, can be sequenced into a curriculum for the purpose of learning a problem that may otherwise be too difficult to learn from scratch. In this article, we present a framework for curriculum learning (CL) in reinforcement learning, and use it to survey and classify existing CL methods in terms of their assumptions, capabilities, and goals. Finally, we use our framework to find open problems and suggest directions for future RL curriculum learning research.

title: MentorNet: Learning Data Driven Curriculum for Very Deep Neural Networks on Corrupted Labels
abstract: Recent deep networks are capable of memorizing the entire data even when the labels are completely random. To overcome the overfitting on corrupted labels, we propose a novel technique of learning another neural network, called MentorNet, to supervise the training of the base deep networks, namely, StudentNet. During training, MentorNet provides a curriculum (sample weighting scheme) for StudentNet to focus on the sample the label of which is probably correct. Unlike the existing curriculum that is usually predefined by human experts, MentorNet learns a data driven curriculum dynamically with StudentNet. Experimental results demonstrate that our approach can significantly improve the generalization performance of deep networks trained on corrupted training data. Notably, to the best of our knowledge, we achieve the best published result on WebVision, a large benchmark containing 2.2 million images of real world noisy labels. The code are at this https URL

title: Curriculum Pre training for End to End Speech Translation
abstract: End to end speech translation poses a heavy burden on the encoder because it has to transcribe, understand, and learn cross lingual semantics simultaneously. To obtain a powerful encoder, traditional methods pre train it on ASR data to capture speech features. However, we argue that pre training the encoder only through simple speech recognition is not enough, and high level linguistic knowledge should be considered. Inspired by this, we propose a curriculum pre training method that includes an elementary course for transcription learning and two advanced courses for understanding the utterance and mapping words in two languages. The difficulty of these courses is gradually increasing. Experiments show that our curriculum pre training method leads to significant improvements on En De and En Fr speech translation benchmarks.

title: Norm Based Curriculum Learning for Neural Machine Translation
abstract: A neural machine translation (NMT) system is expensive to train, especially with high resource settings. As the NMT architectures become deeper and wider, this issue gets worse and worse. In this paper, we aim to improve the efficiency of training an NMT by introducing a novel norm based curriculum learning method. We use the norm (aka length or module) of a word embedding as a measure of 1) the difficulty of the sentence, 2) the competence of the model, and 3) the weight of the sentence. The norm based sentence difficulty takes the advantages of both linguistically motivated and model based sentence difficulties. It is easy to determine and contains learning dependent features. The norm based model competence makes NMT learn the curriculum in a fully automated way, while the norm based sentence weight further enhances the learning of the vector representation of the NMT. Experimental results for the WMT'14 English German and WMT'17 Chinese English translation tasks demonstrate that the proposed method outperforms strong baselines in terms of BLEU score +1.17/+1.56) and training speedup (2.22x/3.33x)

title: On The Power of Curriculum Learning in Training Deep Networks
abstract: Training neural networks is traditionally done by providing a sequence of random mini batches sampled uniformly from the entire training data. In this work, we analyze the effect of curriculum learning, which involves the non uniform sampling of mini batches, on the training of deep networks, and specifically CNNs trained for image recognition. To employ curriculum learning, the training algorithm must resolve 2 problems: (i) sort the training examples by difficulty; (ii) compute a series of mini batches that exhibit an increasing level of difficulty. We address challenge (i) using two methods: transfer learning from some competitive `teacher" network, and bootstrapping. In our empirical evaluation, both methods show similar benefits in terms of increased learning speed and improved final performance on test data. We address challenge (ii) by investigating different pacing functions to guide the sampling. The empirical investigation includes a variety of network architectures, using images from CIFAR 10, CIFAR 100 and subsets of ImageNet. We conclude with a novel theoretical analysis of curriculum learning, where we show how it effectively modifies the optimization landscape. We then define the concept of an ideal curriculum, and show that under mild conditions it does not change the corresponding global minimum of the optimization function.