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DTSTART;TZID=America/New_York:20221209T110000
DTEND;TZID=America/New_York:20221209T130000
DTSTAMP:20260509T035839
CREATED:20221130T213204Z
LAST-MODIFIED:20221130T213204Z
UID:34626-1670583600-1670590800@coe.northeastern.edu
SUMMARY:Bin Sun's PhD Dissertation Defense
DESCRIPTION:“Factorization guided Lightweight Neural Networks for Visual Analysis” \nCommittee: \nProf. Yun Fu (Advisor) \nProf. Ming Shao \nProf. Lili Su \nAbstract: \nDeep learning has become popular in recent years primarily due to powerful computing devices such as GPUs. However\, many applications such as face alignment\, image classification\, and gesture recognition need to be deployed to multimedia devices\, smartphones\, or embedded systems with limited resources. Thus\, there is an urgent need for high-performance but memory-efficient deep learning models. For this\, we design several lightweight deep learning models for different tasks with factorization strategies. \nSpecifically\, we constructed a lightweight face alignment model by proposing a factorization-based deep convolution module named Depthwise Separable Block (DSB) and a light but practical module based on the spatial configuration of the faces. Experiments on four popular datasets verify that Block Mobilenet has better overall performance with less than 1MB storage size.\nBesides the face analysis application\, we also explored a general\, lightweight deep learning module for image classification with low-rank pointwise residual (LRPR) convolution\, called LRPRNet. Essentially\, LRPR aims at using a low-rank approximation to factorize the pointwise convolution while keeping depthwise convolutions as the residual module to rectify the LRPR module. Moreover\, our LRPR is quite general and can be directly applied to many existing network architectures. \nDue to the success of the factorization strategy on image-based data\, we extended factorization on time sequence data for Sign Language Recognition (SLR). We achieved the first rank in the challenge of SLR with the help of our proposed novel Separable Spatial-Temporal Convolution Network (SSTCN)\, which divides a 3D convolution on joint features into several stages \, which help the SSTCN achieve higher accuracy with fewer parameters. \nWe also tried to factorize the features for single image super resolution (SISR). Factorization on features will reduce the feature size in order to reduce the computation costs. However\, the reduction of the spatial size is counter-intuitive for the super resolution task. With our exploration\, we demonstrated a network named Hybrid Pixel-Unshuffled Network (HPUN)\, which factorized the features to achieve the lightweight purpose while keeping high performance. Specifically\, we utilized pixel-unshuffle operation to factorize the input features. After the factorization\, we improved the performance by the grouped convolution\, max-pooling\, and self-residual. The experiments on popular benchmarks showed that the factorization strategy could achieve SOTA performance on SISR.
URL:https://coe.northeastern.edu/event/bin-suns-phd-dissertation-defense/
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DTSTART;TZID=America/New_York:20221209T120000
DTEND;TZID=America/New_York:20221209T133000
DTSTAMP:20260509T035839
CREATED:20221130T212737Z
LAST-MODIFIED:20221130T212840Z
UID:34621-1670587200-1670592600@coe.northeastern.edu
SUMMARY:Alexey Tazin's PhD Dissertation Defense
DESCRIPTION:“Composition of UML Class Diagrams Using Category Theory and External Constraints” \nAbstract:\nIn large software development projects there is always a need for refactoring and optimization of the design. Usually software designs are represented using UML diagrams (e.g class diagrams). A software engineering team may create multiple versions of class diagrams satisfying some external constraints. In some cases\, subdiagrams of the developed diagrams can be selected and combined into one diagram. It is difficult to perform this task manually since manual process is very time consuming\, is prone to human errors\, and is not manageable for large projects. In this dissertation we present an algorithmic support for automating the generation of composed diagrams\, where the composed diagram satisfies a given collection of external constraints and is optimal with respect to a given objective function. The composition of diagrams is based on the colimit operation from category theory. The developed approach was verified experimentally by generating random external constraints (expressed in SPARQL and OWL)\, generating random class diagrams using these external constraints\, generating composed diagrams that satisfy these external constraints\, and computing class diagram metrics for each composed diagram. \nCommittee: \nProf. Mieczyslaw Kokar (Advisor) \nProf. David Kaeli \nDr. Jeff Smith
URL:https://coe.northeastern.edu/event/alexey-tazins-phd-dissertation-defense/
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