1598640480
Lately, posts and tutorials about new deep learning architectures and training strategies have dominated the community. However, one very interesting research area, namely few-shot learning, is not getting the attention it deserves. If we want widespread adoption of ML we need to find ways to train them efficiently, with little data and code. In this tutorial, we will go through a Google Colab Notebook to train an image classification model using only 5 labeled samples per class. Using only 5 exemplary samples is also called 5-shot learning.
Plot showing the top losses from our model trained on only 10 sample images
Don’t forget to check out our Google Colab Notebook for the full code of this tutorial!
Jupyter Notebook (Google Colab)
The full code of this tutorial will be provided as a notebook. Jupyter Notebooks are python programming environments accessible by web browsers and are very useful for fast prototyping and experiments. Colab is a service from Google where you get access to notebooks running on instances for free.
Training a deep learning model can be quite complicated and involve 100s of lines of code. This is where fast.ai comes to the rescue. A library developed by former Kaggler Jeremy Howard specifically aimed to make training deep learning models fast and simple. Using fast.ai we can train and evaluate our classifier with just a few lines of code. Under the hood, fast.ai is using the PyTorch framework.
#data-science #machine-learning #few-shot-learning #deep-learning #data analysis
1598606037
Every week we bring to you the best AI research papers, articles and videos that we have found interesting, cool or simply weird that week.
#ai #this week in ai #ai application #ai news #artificaial inteligance #artificial intelligence #artificial neural networks #deep learning #machine learning #this week in ai
1595398860
Every week we bring to you the best AI research papers, articles and videos that we have found interesting, cool or simply weird that week.Have fun!
#ai #this week in ai #ai application #ai news #artificaial inteligance #artificial intelligence #artificial neural networks #deep learning #machine learning #this week in ai
1617468000
This is a complete guide to start and improve your knowledge of machine learning (ML), artificial intelligence (AI) in 2021 without ANY background in the field and stay up-to-date with the latest news and state-of-the-art techniques!
#learn-ai #ai #artificial-intelligence #machine-learning #deep-learning #learn-machine-learning #youtube-transcripts #youtubers #web-monetization
1593529260
In the previous blog, we looked into the fact why Few Shot Learning is essential and what are the applications of it. In this article, I will be explaining the Relation Network for Few-Shot Classification (especially for image classification) in the simplest way possible. Moreover, I will be analyzing the Relation Network in terms of:
Moreover, effectiveness will be evaluated on the accuracy, time required for training, and the number of required training parameters.
Please watch the GitHub repository to check out the implementations and keep updated with further experiments.
In few shot classification, our objective is to design a method which can identify any object images by analyzing few sample images of the same class. Let’s the take one example to understand this. Suppose Bob has a client project to design a 5 class classifier, where 5 classes can be anything and these 5 classes can even change with time. As discussed in previous blog, collecting the huge amount of data is very tedious task. Hence, in such cases, Bob will rely upon few shot classification methods where his client can give few set of example images for each classes and after that his system can perform classification young these examples with or without the need of additional training.
In general, in few shot classification four terminologies (N way, K shot, support set, and query set) are used.
At this point, someone new to this concept will have doubt regarding the need of support and query set. So, let’s understand it intuitively. Whenever humans sees any object for the first time, we get the rough idea about that object. Now, in future if we see the same object second time then we will compare it with the image stored in memory from the when we see it for the first time. This applied to all of our surroundings things whether we see, read, or hear. Similarly, to recognise new images from query set, we will provide our model a set of examples i.e., support set to compare.
And this is the basic concept behind Relation Network as well. In next sections, I will be giving the rough idea behind Relation Network and I will be performing different experiments on 102-flower dataset.
The Core idea behind Relation Network is to learn the generalized image representations for each classes using support set such that we can compare lower dimensional representation of query images with each of the class representations. And based on this comparison decide the class of each query images. Relation Network has two modules which allows us to perform above two tasks:
Training/Testing procedure:
We can define the whole procedure in just 5 steps.
Few things to know during the training is that we will use only images from the set of selective class, and during the testing, we will be using images from unseen classes. For example, from the 102-flower dataset, we will use 50% classes for training, and rest will be used for validation and testing. Moreover, in each episode, we will randomly select 5 classes to create the support and query set and follow the above 5 steps.
That is all need to know about the implementation point of view. Although the whole process is simple and easy to understand, I’ll recommend reading the published research paper, Learning to Compare: Relation Network for Few-Shot Learning, for better understanding.
#deep-learning #few-shot-learning #computer-vision #machine-learning #deep learning #deep learning
1598640480
Lately, posts and tutorials about new deep learning architectures and training strategies have dominated the community. However, one very interesting research area, namely few-shot learning, is not getting the attention it deserves. If we want widespread adoption of ML we need to find ways to train them efficiently, with little data and code. In this tutorial, we will go through a Google Colab Notebook to train an image classification model using only 5 labeled samples per class. Using only 5 exemplary samples is also called 5-shot learning.
Plot showing the top losses from our model trained on only 10 sample images
Don’t forget to check out our Google Colab Notebook for the full code of this tutorial!
Jupyter Notebook (Google Colab)
The full code of this tutorial will be provided as a notebook. Jupyter Notebooks are python programming environments accessible by web browsers and are very useful for fast prototyping and experiments. Colab is a service from Google where you get access to notebooks running on instances for free.
Training a deep learning model can be quite complicated and involve 100s of lines of code. This is where fast.ai comes to the rescue. A library developed by former Kaggler Jeremy Howard specifically aimed to make training deep learning models fast and simple. Using fast.ai we can train and evaluate our classifier with just a few lines of code. Under the hood, fast.ai is using the PyTorch framework.
#data-science #machine-learning #few-shot-learning #deep-learning #data analysis