Continual Learning

This article aims to provide the reader with an initial idea of how EWC helps overcome catastrophic forgetting in a continual learning setting. In a continuous effort over the years to improve AI and make it just like the human brain, we have come so far!! (Applause for all the…

Adapter Incremental Continual Learning of Efficient Audio Spectrogram Transformers Continual learning involves training neural networks incrementally for new tasks while retaining the knowledge of…arxiv.org Summary 1. Task : Audio classification, task incremental continual learning 2. Authors : Nithish Muthuchamy Selvaraj et al. (Nanyang Technological University) 3. Contribution : Convolutional Adapters for parameter-efficient + Frequency-Time factorized Attention (FTA) for ompute-efficient 4. Performance : SOTA in TI-CL audio event classification 5. Comments :

“Determine the case scenario for our Continual Learning setting is an initial step which affects the complexion of our Continual Learning model.” Continual Learning (CL) is a very broad area. As its name is continual learning, we need the learning to continue, but which learning? Before going deeper into the…

As newer fields emerge within data science and the research is still hard to grasp, sometimes it’s best to talk to the experts and pioneers of the field. Recently, we spoke with Josh Tobin, CEO & Founder of Gantry, about the concept of continual learning and how allowing models to…

This blog is the start of a series of blogs on continual learning. Continual learning refers to the ability of an AI system to learn and adapt to new information and experiences over time, without forgetting previous knowledge. To achieve lifelong learning in AI systems, compositionality and modularity in neural networks have been intensively studied to overcome the challenges of catastrophic forgetting and data efficiency.

Continual Learning The research I am most recently focusing on is Continual Learning. Continual Learning is a method for learning from a continuous stream of data. As the data is continuously given, the data distribution or the task that the data deals with changes, and the model loses some previous knowledge while…

What is BIQA? The concept of blind image quality assessment (BIQA) can be retrieved from 2002. BIQA tackles the problem of “evaluating the visual quality of an image without any reference.” [3] The traditional image quality assessment (IQA) relies on known reference images. However, humans do not need any reference images to assess…

This is an overview of a paper “Towards continual learning in medical imaging” by Chaitanya Baweja, Ben Glocker, and Konstantinos Kamnitsas. What is continual learning? Let’s assume that we have a model which peforms well at some given task, say a brain segmentation task — multi-class segmentation of cerebrospinal fluid, grey matter, and white…