- Data Augmentation | How to use Deep Learning when you have Limited Data | Bharath Raj
- Passenger Screening - How Data Augmentation helped to win
- Batch Norm(alization) & Standardization
- Data Preprocessing
- Feature Exploration/Learning
- Master Data Management (MDM) / Feature Store / Data Lineage / Data Catalog
- Data Augmentation as a best practice for addressing the Overfitting Challenge
- Scale training and validation data for AI applications. After sending us your data via API call, our platform through a combination of human work and review, smart tools, statistical confidence checks and machine learning checks returns scalable, accurate ground truth data.
Data augmentation is the process of using the data you currently have and modifying it in a realistic but randomized way, to increase the variety of data seen during training. As an example for images, slightly rotating, zooming, and/or translating the image will result in the same content, but with a different framing. This is representative of the real-world scenario, so will improve the training. It's worth double-checking that the output of the data augmentation is still realistic. To determine what types of augmentation to use, and how much of it, do some trial and error. Try each augmentation type on a sample set, with a variety of settings (e.g. 1% translation, 5% translation, 10% translation) and see what performs best on the sample set. Once you know the best setting for each augmentation type, try adding them all at the same time. | Deep Learning Course Wiki
Note: In Keras, we can perform transformations using ImageDataGenerator.
Data augmentation adds value to base data by adding information derived from internal and external sources within an enterprise. Data is one of the core assets for an enterprise, making data management essential. Data augmentation can be applied to any form of data, but may be especially useful for customer data, sales patterns, product sales, where additional information can help provide more in-depth insight. Data augmentation can help reduce the manual intervention required to developed meaningful information and insight of business data, as well as significantly enhance data quality.
Data augmentation is of the last steps done in enterprise data management after monitoring, profiling and integration. Some of the common techniques used in data augmentation include:
- Extrapolation Technique: Based on heuristics. The relevant fields are updated or provided with values.
- Tagging Technique: Common records are tagged to a group, making it easier to understand and differentiate for the group.
- Aggregation Technique: Using mathematical values of averages and means, values are estimated for relevant fields if needed
- Probability Technique: Based on heuristics and analytical statistics, values are populated based on the probability of events.
- Essential tips for scaling quality AI data labeling | Damian Rochman - VentureBeat
- Four Mistakes You Make When Labeling Data | Tal Perry Towards Data Science
- Building vs. Buying a training data annotation solution | Labelbox
- Data Labeling: Creating Ground Truth | Astasia Myers - Medium
Labeling typically takes a set of unlabeled data and augments each piece of that unlabeled data with meaningful tags that are informative. Wikipedia
Automation has put low-skill jobs at risk for decades. And self-driving cars, robots, and speech recognition will continue the trend. But, some experts also see new opportunities in the automated age. ...the curation of data, where you take raw data and you clean it up and you have to kind of organize it for machines to ingest Is 'data labeling' the new blue-collar job of the AI era? | Hope Reese - TechRepublic
- 7 Ways to Get High-Quality Labeled Training Data at Low Cost | James Kobielus - KDnuggets
- How to Organize Data Labeling for Machine Learning: Approaches and Tools | AltexSoft KDnuggets
- Natural Language Tools & Services for Text labeling
- ...predict categories (classification)
- Image and video labeling:
- Annotorious the MIT-licensed free web image annotation and labeling tool. It allows for adding text comments and drawings to images on a website. The tool can be easily integrated with only two lines of additional code.
- LabelMe open online tool. Software must assist users in building image databases for computer vision research, its developers note. Users can also download the MATLAB toolbox that is designed for working with images in the LabelMe public dataset.
- Sloth allows users to label image and video files for computer vision research. Face recognition is one of Sloth’s common use cases.
- Object Tagging Tool (VoTT) labeling is one of the model development stages that VoTT supports. This tool also allows data scientists to train and validate object detection models.
- Labelbox build computer vision products for the real world. A complete solution for your training data problem with fast labeling tools, human workforce, data management, a powerful API and automation features.
- Alp’s Labeling Tool macro code allows easy labeling of images, and creates text files compatible with Detectnet / KITTI dataset format.
- imglab graphical tool for annotating images with object bounding boxes and optionally their part locations. Generally, you use it when you want to train an object detector (e.g. a face detector) since it allows you to easily create the needed training dataset.
- VGG Image Annotator (VIA) simple and standalone manual annotation software for image, audio and video
- Demon image annotation plugin allows you to add textual annotations to images by select a region of the image and then attach a textual description, the concept of annotating images with user comments. Integration with JQuery Image Annotation
- FastAnnotationTool (FIAT) enables image data annotation, data augmentation, data extraction, and result visualisation/validation.
- RectLabel an image annotation tool to label images for bounding box object detection and segmentation.
- Audio labeling:
- Praat free software for labeling audio files, mark timepoints of events in the audio file and annotate these events with text labels in a lightweight and portable TextGrid file.
- Speechalyzer a tool for the daily work of a 'speech worker'. It is optimized to process large speech data sets with respect to transcription, labeling and annotation.
- EchoML tool for audio file annotation. It allows users to visualize their data.
This approach entails generating data that imitates real data in terms of essential parameters set by a user. Synthetic data is produced by a generative model that is trained and validated on an original dataset. There are three types of generative models: (1) Generative Adversarial Network (GAN); generative/discriminative, (2) Autoregressive models (ARs); previous values, and (3) Variational Autoencoder (VAE); encoding/decoding.