Continual learning of large language models: A comprehensive survey
The recent success of large language models (LLMs) trained on static, pre-collected,
general datasets has sparked numerous research directions and applications. One such …
general datasets has sparked numerous research directions and applications. One such …
Convolutional Prompting meets Language Models for Continual Learning
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 …
shifting new training data in absence of data from old tasks. Recently pre-trained vision …
Image-Object-Specific Prompt Learning for Few-Shot Class-Incremental Learning
While many FSCIL studies have been undertaken, achieving satisfactory performance,
especially during incremental sessions, has remained challenging. One prominent …
especially during incremental sessions, has remained challenging. One prominent …
Few-shot Tuning of Foundation Models for Class-incremental Learning
For the first time, we explore few-shot tuning of vision foundation models for class-
incremental learning. Unlike existing few-shot class incremental learning (FSCIL) methods …
incremental learning. Unlike existing few-shot class incremental learning (FSCIL) methods …
VL-Few: Vision Language Alignment for Multimodal Few-Shot Meta Learning
Complex tasks in the real world involve different modal models, such as visual question
answering (VQA). However, traditional multimodal learning requires a large amount of …
answering (VQA). However, traditional multimodal learning requires a large amount of …
A streamlined Approach to Multimodal Few-Shot Class Incremental Learning for Fine-Grained Datasets
Few-shot Class-Incremental Learning (FSCIL) poses the challenge of retaining prior
knowledge while learning from limited new data streams, all without overfitting. The rise of …
knowledge while learning from limited new data streams, all without overfitting. The rise of …