ECE PhD Dissertation Defense: Wenqian Liu

PhD Dissertation Defense: Explainable Efficient Models for Computer Vision Applications

Wenqian Liu

Location: Zoom Link

Abstract: State of the art deep learning based models, such as Convolutional Neural Networks (CNNs) and generative models, achieve impressive results, but with their great performance comes great complexity and opacity, huge parametric spaces and little explainability. The criticality of model explainability and output interpretability, manifests clearly in real-time critical decision making processes and human-centred applications, such as in healthcare, security and insurance. Explainability and interpretability are tackled in this thesis, as intrinsic qualities in the model architecture as well as post-hoc improvement on existing models. In the area of frame prediction in video sequences, we introduce DYAN, a novel network with very few parameters, that is easy to train and produces accurate high quality predictions. Another key aspect of DYAN is interpretability, as its encoder-decoder architecture is designed following concepts from systems identification theory and exploits the dynamics-based invariants of the data. We also introduce KW-DYAN, an extension of DYAN that tackles the issue of time lagging in video predictions, by implementing a novel way of quantifying prediction timeliness and proposing a new recurrent network for adaptive temporal sequence prediction. The experimental results show the reduced lagging across datasets, while also performing well in other metrics. In this thesis we also propose the first technique to visually explain VAEs by means of gradient-based attentions, with methods to generate visual attentions from the learned latent space, and also demonstrate such attention explanations serve more than just explaining VAEs. We show how these attention maps can be used to localize anomalies in images, conducting state-of-the-art performance on multiple datasets. We also apply our technique for skin image anomaly detection and diagnosis and achieve competitive quantitative and qualitative results.