Incremental Instance

Incremental instance learning (IIL) focuses on continuously training machine learning models on a stream of individual data points, discarding each after processing, aiming to maintain high accuracy while minimizing "catastrophic forgetting" of previously learned instances. Current research emphasizes developing IIL methods that are robust to delayed labels, concept drift, and the absence of previously seen data, exploring techniques like contrastive learning and knowledge distillation to improve knowledge retention and decision boundary adaptation. The ability to efficiently learn from continuous data streams with minimal memory requirements makes IIL crucial for real-world applications like fraud detection and object tracking, where data arrives sequentially and labeling is expensive or delayed.