Essays about: "data augmentations"
Showing result 16 - 20 of 37 essays containing the words data augmentations.
-
16. Evaluating the effects of data augmentations for specific latent features : Using self-supervised learning
University essay from KTH/Hälsoinformatik och logistikAbstract : Supervised learning requires labeled data which is cumbersome to produce, making it costly and time-consuming. SimCLR is a self-supervising framework that uses data augmentations to learn without labels. This thesis investigates how well cropping and color distorting augmentations work for two datasets, MPI3D and Causal3DIdent. READ MORE
-
17. The research of background removal applied to fashion data : The necessity analysis of background removal for fashion data
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Fashion understanding is a hot topic in computer vision, with many applications having a great business value in the market. It remains a difficult challenge for computer vision due to the immense diversity of garments and a wide range of scenes and backgrounds. READ MORE
-
18. Augmentation for Generalization
University essay from Lunds universitet/Matematik LTHAbstract : To be able to automatically segment cells in microscopic images would give biologist a new tool to gather crucial data in quantities not possible by manual work. This is however not a trivial problem and has proven to be very difficult, especially if the images are in 3D. READ MORE
-
19. Exploring Text Augmentations For Swedish
University essay from Stockholms universitet/Institutionen för data- och systemvetenskapAbstract : Text Augmentation has been given very little attention in the Swedish text domains. Hence, applying popular techniques like EDA, Type-specific synonym replacement and Text Fragmentation has not been explored on datasets such as linguistic acceptability and aspect-based sentiment analysis. READ MORE
-
20. Noisy recognition of perceptual mid-level features in music
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Self-training with noisy student is a consistency-based semi-supervised self- training method that achieved state-of-the-art accuracy on ImageNet image classification upon its release. It makes use of data noise and model noise when fitting a model to both labelled data and a large amount of artificially labelled data. READ MORE