Deep knowledge transfer for generalization across tasks and domains under data scarcity

Over the last decade, deep learning approaches have achieved tremendous performance in a wide variety of fields, e.g., computer vision and natural language understanding, and across several sectors such as healthcare, industrial manufacturing, and driverless mobility. Most deep learning successes were accomplished in learning scenarios fulfilling the two following requirements. First, large amounts of data are available for training the deep learning model and there are no access restrictions to the data. Second, the data used for training and testing is independent and identically distributed (i.i.d.). However, many real-world applications infringe at least one of the aforementioned requirements, which results in challenging learning problems. The present thesis comprises four contributions to address four such learning problems. In each contribution, we propose a novel method and empirically demonstrate its effectiveness for the corresponding problem setting. The first part addresses the underexplored intersection of the few-shot learning and the one-class classification problems. In this learning scenario, the model has to learn a new task using only a few examples from only the majority class, without overfitting to the few examples or to the majority class. This learning scenario is faced in real-world applications of anomaly detection where data is scarce. We propose an episode sampling technique to adapt meta-learning algorithms designed for class-balanced few-shot classification to the addressed few-shot one-class classification problem. This is done by optimizing for a model initialization tailored for the addressed scenario. In addition, we provide theoretical and empirical analyses to investigate the need for second-order derivatives to learn such parameter initializations. Our experiments on 8 image and time-series datasets, including a real-world dataset of industrial sensor readings, demonstrate the effectiveness of our method. The second part tackles the intersection of the continual learning and the anomaly detection problems, which we are the first to explore, to the best of our knowledge. In this learning scenario, the model is exposed to a stream of anomaly detection tasks, i.e., only examples from the normal class are available, that it has to learn sequentially. Such problem settings are encountered in anomaly detection applications where the data distribution continuously changes. We propose a meta-learning approach that learns parameter-specific initializations and learning rates suitable for continual anomaly detection. Our empirical evaluations show that a model trained with our algorithm is able to learn up 100 anomaly detection tasks sequentially with minimal catastrophic forgetting and overfitting to the majority class. In the third part, we address the domain generalization problem, in which a model trained on several source domains is expected to generalize well to data from a previously unseen target domain, without any modification or exposure to its data. This challenging learning scenario is present in applications involving domain shift, e.g., different clinical centers using different MRI scanners or data acquisition protocols. We assume that learning to extract a richer set of features improves the transfer to a wider set of unknown domains. Motivated by this, we propose an algorithm that identifies the already learned features and corrupts them, hence enforcing new feature discovery. We leverage methods from the explainable machine learning literature to identify the features, and apply the targeted corruption on multiple representation levels, including input data and high-level embeddings. Our extensive empirical evaluation shows that our approach outperforms 18 domain generalization algorithms on multiple benchmark datasets. The last part of the thesis addresses the intersection of domain generalization and data-free learning methods, which we are the first to explore, to the best of our knowledge. Hereby, we address the learning scenario where a model robust to domain shift is needed and only models trained on the same task but different domains are available instead of the original datasets. This learning scenario is relevant for any domain generalization application where the access to the data of the source domains is restricted, e.g., due to concerns about data privacy concerns or intellectual property infringement. We develop an approach that extracts and fuses domain-specific knowledge from the available teacher models into a student model robust to domain shift, by generating synthetic cross-domain data. Our empirical evaluation demonstrates the effectiveness of our method which outperforms ensemble and data-free knowledge distillation baselines. Most importantly, the proposed approach substantially reduces the gap between the best data-free baseline and the upper-bound baseline that uses the original private data

Learning Meta Model for Zero- and Few-shot Face Anti-spoofing

Face anti-spoofing is crucial to the security of face recognition systems. Most previous methods formulate face anti-spoofing as a supervised learning problem to detect various predefined presentation attacks, which need large scale training data to cover as many attacks as possible. However, the trained model is easy to overfit several common attacks and is still vulnerable to unseen attacks. To overcome this challenge, the detector should: 1) learn discriminative features that can generalize to unseen spoofing types from predefined presentation attacks; 2) quickly adapt to new spoofing types by learning from both the predefined attacks and a few examples of the new spoofing types. Therefore, we define face anti-spoofing as a zero- and few-shot learning problem. In this paper, we propose a novel Adaptive Inner-update Meta Face Anti-Spoofing (AIM-FAS) method to tackle this problem through meta-learning. Specifically, AIM-FAS trains a meta-learner focusing on the task of detecting unseen spoofing types by learning from predefined living and spoofing faces and a few examples of new attacks. To assess the proposed approach, we propose several benchmarks for zero- and few-shot FAS. Experiments show its superior performances on the presented benchmarks to existing methods in existing zero-shot FAS protocols.Comment: Accepted by AAAI202

Zero-Shot Cross-Lingual Transfer with Meta Learning

Learning what to share between tasks has been a topic of great importance recently, as strategic sharing of knowledge has been shown to improve downstream task performance. This is particularly important for multilingual applications, as most languages in the world are under-resourced. Here, we consider the setting of training models on multiple different languages at the same time, when little or no data is available for languages other than English. We show that this challenging setup can be approached using meta-learning, where, in addition to training a source language model, another model learns to select which training instances are the most beneficial to the first. We experiment using standard supervised, zero-shot cross-lingual, as well as few-shot cross-lingual settings for different natural language understanding tasks (natural language inference, question answering). Our extensive experimental setup demonstrates the consistent effectiveness of meta-learning for a total of 15 languages. We improve upon the state-of-the-art for zero-shot and few-shot NLI (on MultiNLI and XNLI) and QA (on the MLQA dataset). A comprehensive error analysis indicates that the correlation of typological features between languages can partly explain when parameter sharing learned via meta-learning is beneficial.Comment: Accepted as long paper in EMNLP2020 main conferenc