Back-Projective Priming: Toward Efficient 3d Model-based Object Recognition via Preemptive Top-down Constraints

This thesis introduces back-projective priming, a computer vision technique that synergistically fuses object recognition and pose estimation by augmenting 3D models with geometric constraints. It also enables the use of image features too indistinct for use by other model fitting algorithms such as geometric hashing. To efficiently accommodate features that do not provide a scale attribute, we've developed a "match pair" finding heuristic called second-order similarity that reduces model fitting time complexity from a worst case of O(N^2) to O(N*Log(N)). An object recognition problem that is simple, practical, and well explored by other researchers is the problem of locating electrical outlets from the vantage point of a mobile robot. To demonstrate the relative merits of back-projective priming, we use it to build a system capable of locating generic electrical outlets in unmapped environments. Compared to our baseline algorithm, back-projective priming is shown to provide superior sensitivity when dealing with the challenges of low contrast, perspective distortion, partial occlusion, and decoys

A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems

Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original training context, and systems will instead need to adapt to novel distributions and tasks while deployed. This critical gap may be addressed through the development of "Lifelong Learning" systems that are capable of 1) Continuous Learning, 2) Transfer and Adaptation, and 3) Scalability. Unfortunately, efforts to improve these capabilities are typically treated as distinct areas of research that are assessed independently, without regard to the impact of each separate capability on other aspects of the system. We instead propose a holistic approach, using a suite of metrics and an evaluation framework to assess Lifelong Learning in a principled way that is agnostic to specific domains or system techniques. Through five case studies, we show that this suite of metrics can inform the development of varied and complex Lifelong Learning systems. We highlight how the proposed suite of metrics quantifies performance trade-offs present during Lifelong Learning system development - both the widely discussed Stability-Plasticity dilemma and the newly proposed relationship between Sample Efficient and Robust Learning. Further, we make recommendations for the formulation and use of metrics to guide the continuing development of Lifelong Learning systems and assess their progress in the future.Comment: To appear in Neural Network

Attention-Dependent and Continuous Representation Learning using Deep Autoencoders

The ability to learn low-dimensional representations of high-dimensional data is foundational to general cognitive functions such as comparison, abstraction, prediction, and planning. Additionally, systems that can learn in an unsupervised continuous fashion should be able to automatically adapt to changes in the target distribution and reach levels of accuracy beyond what can be achieved using static datasets. To achieve these capabilities, we propose two approaches: Attention-Dependent Autoencoders (ADA), and Autoencoder Leader-follower Clustering (ALC). ADA produces vector embeddings of images, broadly analogous to the declarative memory engrams formed by the entorhinal-cortex hippocampal system. In order for these embedded representations to be useful within a cognitive architecture, they must be compact, their relative vector distances should reflect semantic distance, and the encoding process should allow modulation via attentional mechanisms. It must also be possible to decode the embeddings back to the input space, and critically, the reconstructions must preserve the semantics of the original data with respect to the cognitive system as a whole. To address these goals, we use “conservational loss” to train an autoencoder that generates reconstructions which conserve the activations of a single-class semantic segmenter, which we treat as a visual attention model. The resulting autoencoder preserves class-specific regions of images and can be modulated using the segmentation masks as attention vectors. The semantic embeddings produced by the encoder are shown to be amenable to distance metrics, and the reconstructions of the decoder shown to preserve the target-class better than a generic autoencoder, even appearing competitive with JPEG at lower bit-rates. We also suggest the use of autoencoder conservational loss as a post-2 deployment error metric for the attention-model and discuss the broader implications of conservational loss in general. ALC addresses the catastrophic interference problem in continuous learning by using a gateless mixture-of-experts generated through autoencoder cloning, such that clones model different portions of the sample distribution in a shifting landscape built through a leader-follower clustering algorithm, with reconstruction error serving as the distance metric. To address scalability issues, ALC employs shared pseudo-rehearsal so the autoencoders of the ensemble can gradually consolidate their learned patterns into fewer autoencoders

Back-Projective Priming: Toward Efficient 3d Model-based Object Recognition via Preemptive Top-down Constraints

"This thesis introduces back-projective priming, a computer vision technique that synergistically fuses object recognition and pose estimation by augmenting 3D models with geometric constraints. It also enables the use of image features too indistinct for use by other model fitting algorithms such as geometric hashing. To efficiently accommodate features that do not provide a scale attribute, we've developed a ""match pair"" finding heuristic called second-order similarity that reduces model fitting time complexity from a worst case of O(N^2) to O(N*Log(N)). An object recognition problem that is simple, practical, and well explored by other researchers is the problem of locating electrical outlets from the vantage point of a mobile robot. To demonstrate the relative merits of back-projective priming, we use it to build a system capable of locating generic electrical outlets in unmapped environments. Compared to our baseline algorithm, back-projective priming is shown to provide superior sensitivity when dealing with the challenges of low contrast, perspective distortion, partial occlusion, and decoys.

A domain-agnostic approach for characterization of lifelong learning systems

Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to “real world” events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original training context, and systems will instead need to adapt to novel distributions and tasks while deployed. This critical gap may be addressed through the development of “Lifelong Learning” systems that are capable of (1) Continuous Learning, (2) Transfer and Adaptation, and (3) Scalability. Unfortunately, efforts to improve these capabilities are typically treated as distinct areas of research that are assessed independently, without regard to the impact of each separate capability on other aspects of the system. We instead propose a holistic approach, using a suite of metrics and an evaluation framework to assess Lifelong Learning in a principled way that is agnostic to specific domains or system techniques. Through five case studies, we show that this suite of metrics can inform the development of varied and complex Lifelong Learning systems. We highlight how the proposed suite of metrics quantifies performance trade-offs present during Lifelong Learning system development — both the widely discussed Stability-Plasticity dilemma and the newly proposed relationship between Sample Efficient and Robust Learning. Further, we make recommendations for the formulation and use of metrics to guide the continuing development of Lifelong Learning systems and assess their progress in the future