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An Application Of Deep Learning Models To Automate Food Waste Classification, Alejandro Zachary Espinoza
An Application Of Deep Learning Models To Automate Food Waste Classification, Alejandro Zachary Espinoza
Dissertations and Theses
Food wastage is a problem that affects all demographics and regions of the world. Each year, approximately one-third of food produced for human consumption is thrown away. In an effort to track and reduce food waste in the commercial sector, some companies utilize third party devices which collect data to analyze individual contributions to the global problem. These devices track the type of food wasted (such as vegetables, fruit, boneless chicken, pasta) along with the weight. Some devices also allow the user to leave the food in a kitchen container while it is weighed, so the container weight must also …
Sensory Relevance Models, Walt Woods
Sensory Relevance Models, Walt Woods
Dissertations and Theses
This dissertation concerns methods for improving the reliability and quality of explanations for decisions based on Neural Networks (NNs). NNs are increasingly part of state-of-the-art solutions for a broad range of fields, including biomedical, logistics, user-recommendation engines, defense, and self-driving vehicles. While NNs form the backbone of these solutions, they are often viewed as "black box" solutions, meaning the only output offered is a final decision, with no insight into how or why that particular decision was made. For high-stakes fields, such as biomedical, where lives are at risk, it is often more important to be able to explain a …
Design Of A Canine Inspired Quadruped Robot As A Platform For Synthetic Neural Network Control, Cody Warren Scharzenberger
Design Of A Canine Inspired Quadruped Robot As A Platform For Synthetic Neural Network Control, Cody Warren Scharzenberger
Dissertations and Theses
Legged locomotion is a feat ubiquitous throughout the animal kingdom, but modern robots still fall far short of similar achievements. This paper presents the design of a canine-inspired quadruped robot named DoggyDeux as a platform for synthetic neural network (SNN) research that may be one avenue for robots to attain animal-like agility and adaptability. DoggyDeux features a fully 3D printed frame, 24 braided pneumatic actuators (BPAs) that drive four 3-DOF limbs in antagonistic extensor-flexor pairs, and an electrical system that allows it to respond to commands from a SNN comprised of central pattern generators (CPGs). Compared to the previous version …
The Improvement Of Machine Learning Accuracies Through Transfer Learning, Jordan T. Le
The Improvement Of Machine Learning Accuracies Through Transfer Learning, Jordan T. Le
University Honors Theses
Pretrained models could be reused in a way that allows for improvement in training accuracy. Training a model from scratch takes time. The goal is improving accuracy and minimizing the loss across individual epochs. The hypothesis is that transfer learning could potentially improve on the rate of accuracy and speed of training per epoch iteration.
An Evaluation Of Vgg16 And Yolo V3 On Hand-Drawn Images, Lee Hoang
An Evaluation Of Vgg16 And Yolo V3 On Hand-Drawn Images, Lee Hoang
University Honors Theses
This thesis evaluates the accuracy and performance of VGG16, a convolutional neural network (CNN), and YOLO v3, an object detector, on a dataset of 1000 hand-drawn images. Unlike with photographs, which possess high amounts of detail, sketches tend to lack much detail aside from the freehand lines that comprise them. This is further detailed in prior works about Sketch-based Image Retrieval (SBIR), a classification task to map photographs to sketches; and SketchParse, a CNN that analyzes sketch features and assigns captions. In this paper, I show the differences in classification accuracy between VGG16 and YOLO v3. The former model, pretrained …
Localizing Little Landmarks With Transfer Learning, Sharad Kumar
Localizing Little Landmarks With Transfer Learning, Sharad Kumar
Dissertations and Theses
Locating a small object in an image -- like a mouse on a computer desk or the door handle of a car -- is an important computer vision problem to solve because in many real life situations a small object may be the first thing that gets operated upon in the image scene. While a significant amount of artificial intelligence and machine learning research has focused on localizing prominent objects in an image, the area of small object detection has remained less explored. In my research I explore the possibility of using context information to localize small objects in an …
A Dendritic Transfer Function In A Novel Fully-Connected Layer, Mark Robert Musil
A Dendritic Transfer Function In A Novel Fully-Connected Layer, Mark Robert Musil
University Honors Theses
Dendritic branch operations in pyramidal neurons are well understood in-vivo but their potential as computational assets in deep neural networks has not been explored. The pre-processing which dendrites perform may be able to decrease the error of an artificial neuron because each dendrite serves as an independent filtering mechanism which may prevent false positives. In order to test this hypothesis, a fully-connected layer implementing the dendritic transfer function is defined and used to replace the final fully-connected layer used in a standard CNN (convolutional neural network). Results show that the defined algorithm is not able to predict better than chance …