Understanding Local Loss, Focal Loss, and Gradient Blending in Multi-Task Learning

Overview

Multi-Task Learning (MTL) has emerged as a powerful approach to building machine learning models that can perform multiple tasks simultaneously. In this blog post, we will delve into key concepts in MTL: Local Loss, Focal Loss and Gradient Blending. These concepts are crucial for ensuring that the model learns effectively across all tasks without compromising the performance of any individual task.

Introduction to Multi-Task Learning (MTL)

Multi-Task Learning (MTL) is a paradigm where a single model is trained to perform multiple tasks at once. Unlike traditional models that focus on a single objective, MTL leverages the commonalities and differences among tasks to improve learning efficiency and generalization. A typical MTL setup might involve tasks such as:

• Harmful Content Detection: Detection harmful contents in social media posts, and categorizing them into predefined classes (Violence, nudity, hate speech etc), which also has multiple modalities per post(text and image or video).
• Image Classification: Categorizing images into predefined classes.
• Object Detection: Identifying and localizing objects within an image.
• Sentiment Analysis: Determining the sentiment (positive, negative, neutral) of a text.