Over-fitting is where your model does a poor job of predicting the future, because it is overfitting. It’s not uncommon for an algorithm to perform well in testing but overfit when used in production. Overfitting is a very common practice and is when you over fit the data set used to train a model. This happens because the algorithm you are using doesn’t take into account enough variables in the data set.
How do we get our data to look correct and to learn all there is to learn? The answer is provided by Data Augmentation. Data Augmentation helps us make sense of the data, use it as a model, and then use the model to create predictions. In this article we will know everything about once robust technique that will improve your AI model accuracy using data augmentation.
So What is Data Augmentation and how does AugLy helps ?
Data augmentation is a technique that generates new samples from an existing sample in order to increase the size of the dataset. It is widely used in machine learning competitions, such as Kaggle, to boost performance. However, it can be time consuming; therefore we started creating a tool that can automatically generate augmented data based on the user’s requirements there for AugLy comes to rescue.
It is a powerful tool that can help increase the generalizability of your models, leading to more robust results in the real world.
“AugLy is a tool to help you make more data.”
AugLy is a data augmentation platform. It helps you expand your training data by generating new sentences that sound like the ones you already have.
This is useful for a range of tasks, like training chatbots or speech recognition systems, where you need a lot of sample data to get good performance.
But it’s not just for bots and voice tech—you can use it anywhere that you need a lot of sentences that all sound alike—like product listings for an e-commerce site, song descriptions for an online music service, or articles about Dwayne “The Rock” Johnson.
This system can be used by researchers to improve the performance of their models, and can also be applied to problems in a wide range of fields, including NLP, computer vision, biomedicine, and more.
Lets see some Advantage, Benefits and Challenges of AugLy :
There are several challenges in data augmentation. First, a large amount of data must be available to create accurate models. Second, you must identify and label all of the data correctly. Third, you must know how to properly augment it.
AugLy is a new tool that solves these challenges by creating additional labeled samples from existing ones.
AugLy has several advantages over other tools:
First, AugLy is fast because it does not require pre-processing or post-processing steps like some tools do. Second, it can generate large volumes of augmented data quickly and easily with low memory usage due to its efficient use of RAM.
Finally, AugLy provides a way to reduce the size of your dataset while still maintaining high accuracy by allowing users to augment certain classes more than others based on their needs and preferences (if they want more or less examples).
It has several benefits:
First, it’s extremely easy to use. You can upload your existing images and it will apply the AugLy algorithm to produce new images for your model to train on.
Second, it is robust to variations in input. It doesn’t require extra images and can work with as little as a single image. This means you can generate diverse images from just one or two samples.
Third, it is scalable. Since AugLy uses deep learning, it’s more flexible than other methods for augmenting data. You can use it on any kind of input including text and audio.
How does AugLy works ?
AugLy is a framework for data augmentation based on the idea of “virtual perturbations”. The basic idea is to create a set of operations that can be applied to images to alter them without actually changing the pixels themselves. These operations include things like flipping an image upside down, rotating it by 90 degrees, or adding noise to it.
AugLy includes various types of data augmentation methods like Synonym Replacement, Random Insertion, Random Swap, Random Deletion, Text Scrambling, Contextual Word Embeddings and Backtranslation.
Synonym Replacement replaces words or word chunks in the sentences with their synonyms. This method is useful to improve model performance on unseen words.
Random Insertion inserts random words in the sentence. It helps model to generalize better by learning the context between different words in a sentence.
Random Swap swaps two words in the sentence randomly and helps model learn the semantics of a sentence.
Random Deletion deletes one or more words from a sentence randomly and helps model learn which words are more important for a sentence.
Text Scrambling scrambles text within a word or phrase and also scrambles text between two separate phrases. It helps model recognize individual lexical units as well as phrases in text.
Contextual Word Embeddings generates new sentences by replacing words with its synonyms from pre-trained contextual word embed
There are two different ways AugLy works:
- You can ask it to add new sentences that are similar to your original ones, and these will be generated using a language model like GPT-2.
- You can ask it to add new words to existing sentences using synonyms or paraphrases from an external resource like WordNet or Spacy; in this case, we take advantage of the pre-trained models provided by HuggingFace in their library called Transformers.
What are the use cases if AugLy :
AugLy is a great tool for any business that deals with text.
For example, if you’re running a social media program, AugLy can help you get more mileage out of your ideas. Say you have a new product or service to announce: once you’ve decided on the headline, AugLy can help you create a variety of posts that say the same thing but in different ways. You can then schedule those posts throughout the month to reach the widest possible audience.
Another example is customer support. If you have a frequently asked question, like “How do I activate my account?” AugLy can help you generate variety in the answers you send out to customers.
If you have time-sensitive content that needs to be updated regularly—like news headlines—AugLy will make sure every update is fresh and different from previous ones. This ensures your readers don’t get bored of seeing the same thing over and over again.
If your business has ever felt like it’s stuck in a rut, AugLy can help get things going again by rekindling creativity and generating new ideas for future endeavors!
AugLy’s implementation :
#installing AugLY
pip install augly
#importing required supporting libraries
import augly.utils as util
import augly.image as ima
from IPython.display import display
import augly.text as textaugFind the entire Githup report here for your reference.
AugLy allows you to use your existing data to generate new, similar but unique samples that can help your models generalize better. If you work with text or speech data, please give AugLy a try!
In the end, it’s nice to see that dedicated data augmentation companies like AugLy can provide the tools needed by researchers to streamline their research processes. Hopefully, these custom augmenters can help with some of the most time consuming (and critical) areas of a researcher’s workflow so that they can focus on more important aspects of their studies. Hope you liked this article at MLDots.
