What is Dataset Distillation?
Dataset Distillation is the process of choosing a subset of data samples that capture the most essential and representative aspects of the original dataset. It's used to reduce the processing needs of the training operations while retaining critical information.
What is Mask R-CNN?
Mask R-CNN, or Mask Region-based Convolutional Neural Network, is an extension of the Faster R-CNN object detection method, which is used in computer vision for both object recognition and instance segmentation.
Autoencoders in Computer Vision
An autoencoder is a type of artificial neural network that is used to learn data encodings unsupervised. The autoencoder must examine the input and create a function capable of transforming a specific instance of that data into a meaningful representation.
What is Transfer Learning?
Assume you have an issue you want to tackle with computer vision but just a few images to base your new model on. What are your options?
Zero-shot learning in Computer Vision/Vision AI
Zero-shot learning (ZSL) is a machine learning technique that enables a model to categorise items from previously unseen classes without getting any explicit training for those classes.
How to Split Dataset in Machine Learning?
To prevent overfitting and to correctly evaluate your model, divide your data into train, validation, and test batches
What is EfficientNet?
First introduced in 2019 by a team of Google AI researchers, quickly became a go-to architecture for many difficult tasks
What is a neural network?
The development of neural networks is an active subject of study, as academics and businesses attempt to find more efficient ways to handle complicated problems using machine learning.
What is Knowledge Distillation?
Deep neural networks have grown in popularity for a variety of applications ranging from recognising items in images using object detection models to creating language using GPT models. Deep learning models, on the other hand, are frequently huge and computationally costly, making them challenging to deploy on resource-constrained devices like mobile phones or embedded systems. Knowledge distillation solves this issue by condensing a huge, complicated neural network into a smaller, simpler one while retaining its performance.