Essays about: "data augmentations"
Showing result 1 - 5 of 22 essays containing the words data augmentations.
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1. Self-Supervised Learning for Tabular Data: Analysing VIME and introducing Mix Encoder
University essay from Lunds universitet/Fysiska institutionenAbstract : We introduce Mix Encoder, a novel self-supervised learning framework for deep tabular data models based on Mixup [1]. Mix Encoder uses linear interpolations of samples with associated pretext tasks to form useful pre-trained representations. READ MORE
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2. Data Augmentation for Object Detection using Deep Reinforcement Learning
University essay from Lunds universitet/Institutionen för reglerteknikAbstract : Data augmentation is a concept which is used to improve machine learning models for computer vision tasks. It is usually done by firstly, defining a set of functions which transforms images and secondly, applying a random selection of these functions on the images. READ MORE
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3. Data Augmentations for Improving Vision-Based Damage Detection : in Land Transport Infrastructure
University essay from KTH/Lantmäteri – fastighetsvetenskap och geodesiAbstract : Crack, a typical term most people know, is a common form of distress or damage in road pavements and railway sleepers. It poses significant challenges to their structural integrity, safety, and longevity. Over the years, researchers have developed various data-driven technologies for image-based crack detection in road and sleeper applications. READ MORE
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4. Improving The Accuracy Of Plant Leaf Disease Detection And Classification In Images Of Plant Leaves: : By Exploring Various Techniques with the MobileNetV2 Model
University essay from Blekinge Tekniska Högskola/Institutionen för datavetenskapAbstract : In the most recent years, many deep learning models have been used to identify and classify diseases of plant leaves by inputting plant leaf images as input to the model. However, there is still a gap in research on how to improve the accuracy of the deep learning models of plant leaf diseases. READ MORE
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5. Automatic 3D Segmentation in CT images of Congenital Heart Defects using Deep Learning
University essay from Lunds universitet/Matematik LTHAbstract : 3D segmentations of hearts with congenital heart defects are routinely used today. They are used to study hearts and to prepare before surgery which makes them an important part of patient care. The 3D segmentations are usually created manually, which is a time-consuming process. READ MORE