Articles about "Incremental Learning"
Table of Contents
Incremental learning is a way for machines to learn new information over time without forgetting what they already know. This is important because as new things come up, the machine should keep its old knowledge while adding new details.
Challenges in Incremental Learning
There are two main problems that come up during this process:
- E2O: This happens when a machine mislabels an old item as something that doesn't exist anymore.
- O2E: In this case, the machine wrongly labels an old item or something new as a new category.
These issues can lead to mistakes, where the machine favors new information too much and forgets the old.
Solutions to Improve Learning
To make incremental learning better, researchers have come up with different methods. One way is to help the machine remember old information while learning new things. This is often done using various techniques that support how the machine processes data.
In other cases, special adjustments can be made to ensure that the machine learns correctly from its past without getting confused about new information. This helps the machine stay accurate and reliable.
Applications
Incremental learning is used in many areas, such as recognizing objects in images or managing different categories of information. By continuously learning, machines can become more effective and keep improving their abilities over time.