A zero­shot learning method for recognizing objects using low­power devices

Zero­Shot Learning (ZSL) has been a subject of increasing interest due to its revolutionary paradigm that simulates human behavior in recognizing objects that have never seen before. The ZSL models must be capable of recognizing classes that do not appear during training, using only the provided textual descriptions of the unseen classes as an aid. Despite the vast benchmarking around the ZSL paradigm, few works have assessed the computational performance of the developed strategy regarding inference time. Furthermore, no work has evaluated the effects of using different CNN architectures, such as lightweight architectures, apart from the de facto standard ResNet101 architecture, and the feasibility of deploying zero­shot learning approaches in a real­world scenario, particularly when using low­power devices. Consequently, in this dissertation, we carried out an extensive benchmarking toward analyzing the impact of using lightweight CNN architectures on ZSL performance, allowing us to perceive how the ZSL methods perform in real­world scenarios, mainly when run in low­power devices. Our experimental results demonstrate that the impact on the ZSL accuracy is not significant when a lightweight architecture is adopted, indicating the effectiveness of such low­power devices in performing ZSL methods.O Zero­Shot Learning (ZSL) tem sido uma área de interesse crescente devido ao seu paradigma revolucionário que visa simular o comportamento humano na tarefa de reconhecimento de objetos que nunca foram vistos anteriormente. Os modelos de ZSL devem ser capazes de reconhecer classes de objetos que nunca tenham sido vistos durante o treino do classificador, tendo apenas como auxílio para a previsão de classes desconhecidas, descrições textuais das mesmas. Apesar da vasta literatura existente em torno da temática do ZSL, são poucos os trabalhos que avaliam o desempenho computacional dos métodos desenvolvidos, no que diz respeito ao tempo dispendido na fase de inferência. Até à data, nenhum trabalho avaliou o impacto do uso de arquiteturas menos complexas e com menor custo computacional nos métodos de ZSL, para além da arquitetura padrão de facto ResNet101. Além do mais, a viabilidade de implementar os métodos de ZSL em aplicações do mundo real, particularmente fazendo uso de dispositivos de baixa capacidade computacional, ainda não foi estudada. Assim, esta dissertação faz a avaliação de diferentes métodos de ZSL no que respeita ao impacto do uso de arquiteturas menos complexas de redes neuronais convolucionais no desempenho geral dos métodos de ZSL. Desta forma, é possível ficar ciente do comportamento dos métodos de ZSL em cenários reais, principalmente quando implementados em dispositivos de baixa capacidade computacional. Os resultados obtidos demonstraram que o impacto no valor da precisão dos métodos de ZSL não é significativo quando são adotadas arquiteturas menos complexas para efeitos de extração de caraterísticas das imagens, sendo possível inferir que os métodos de ZSL são capazes de operar em tempo real em dispositivos de baixa capacidade computacional

Learning Transferable Knowledge Through Embedding Spaces

The unprecedented processing demand, posed by the explosion of big data, challenges researchers to design efficient and adaptive machine learning algorithms that do not require persistent retraining and avoid learning redundant information. Inspired from learning techniques of intelligent biological agents, identifying transferable knowledge across learning problems has been a significant research focus to improve machine learning algorithms. In this thesis, we address the challenges of knowledge transfer through embedding spaces that capture and store hierarchical knowledge. In the first part of the thesis, we focus on the problem of cross-domain knowledge transfer. We first address zero-shot image classification, where the goal is to identify images from unseen classes using semantic descriptions of these classes. We train two coupled dictionaries which align visual and semantic domains via an intermediate embedding space. We then extend this idea by training deep networks that match data distributions of two visual domains in a shared cross-domain embedding space. Our approach addresses both semi-supervised and unsupervised domain adaptation settings. In the second part of the thesis, we investigate the problem of cross-task knowledge transfer. Here, the goal is to identify relations and similarities of multiple machine learning tasks to improve performance across the tasks. We first address the problem of zero-shot learning in a lifelong machine learning setting, where the goal is to learn tasks with no data using high-level task descriptions. Our idea is to relate high-level task descriptors to the optimal task parameters through an embedding space. We then develop a method to overcome the problem of catastrophic forgetting within continual learning setting of deep neural networks by enforcing the tasks to share the same distribution in the embedding space. We further demonstrate that our model can address the challenges of domain adaptation in the continual learning setting. Finally, we consider the problem of cross-agent knowledge transfer in the third part of the thesis. We demonstrate that multiple lifelong machine learning agents can collaborate to increase individual performance by sharing learned knowledge in an embedding space without sharing private data through a shared embedding space. We demonstrate that despite major differences, problems within the above learning scenarios can be tackled through learning an intermediate embedding space that allows transferring knowledge effectively