Prompt-based continual learning is an emerging direction in leveraging pre-trained
knowledge for downstream continual learning, and has almost reached the performance …

Continual learning (CL) aims to incrementally learn different tasks (such as classification) in
a non-stationary data stream without forgetting old ones. Most CL works focus on tackling …

Prompt-tuning methods for Continual Learning (CL) freeze a large pre-trained model and
train a few parameter vectors termed prompts. Most of these methods organize these vectors …

Traditional online continual learning (OCL) research has primarily focused on mitigating
catastrophic forgetting with fixed and limited storage allocation throughout an agent's …

Nowadays, real-world applications often face streaming data, which requires the learning
system to absorb new knowledge as data evolves. Continual Learning (CL) aims to achieve …

Continual Learning (CL) enables machine learning models to learn from continuously
shifting new training data in absence of data from old tasks. Recently pre-trained vision …

Contemporary continual learning approaches typically select prompts from a pool, which
function as supplementary inputs to a pre-trained model. However, this strategy is hindered …

Continual learning, also known as lifelong learning or incremental learning, refers to the
process by which a model learns from a stream of incoming data over time. A common …

In recent years, prompt-based methods have emerged as a promising direction for continual
learning, demonstrating impressive performance across various benchmarks. These …

Continual learning based on prompt tuning creates a key-value pool, where these key-value
pairs are called prompts. Prompts are retrieved using input images as queries and input into …