Revisiting class-incremental learning with pre-trained models: Generalizability and adaptivity are all you need
Class-incremental learning (CIL) aims to adapt to emerging new classes without forgetting
old ones. Traditional CIL models are trained from scratch to continually acquire knowledge …
old ones. Traditional CIL models are trained from scratch to continually acquire knowledge …
Revisiting class-incremental learning with pre-trained models: Generalizability and adaptivity are all you need
Class-incremental learning (CIL) aims to adapt to emerging new classes without forgetting
old ones. Traditional CIL models are trained from scratch to continually acquire knowledge …
old ones. Traditional CIL models are trained from scratch to continually acquire knowledge …
Class-incremental learning: A survey
Deep models, eg, CNNs and Vision Transformers, have achieved impressive achievements
in many vision tasks in the closed world. However, novel classes emerge from time to time in …
in many vision tasks in the closed world. However, novel classes emerge from time to time in …
Expandable subspace ensemble for pre-trained model-based class-incremental learning
Abstract Class-Incremental Learning (CIL) requires a learning system to continually learn
new classes without forgetting. Despite the strong performance of Pre-Trained Models …
new classes without forgetting. Despite the strong performance of Pre-Trained Models …
Few-shot class incremental learning with attention-aware self-adaptive prompt
Abstract Few-Shot Class-Incremental Learning (FSCIL) models aim to incrementally learn
new classes with scarce samples while preserving knowledge of old ones. Existing FSCIL …
new classes with scarce samples while preserving knowledge of old ones. Existing FSCIL …
Continual learning with pre-trained models: A survey
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 …
system to absorb new knowledge as data evolves. Continual Learning (CL) aims to achieve …
Mamba-fscil: Dynamic adaptation with selective state space model for few-shot class-incremental learning
Few-shot class-incremental learning (FSCIL) confronts the challenge of integrating new
classes into a model with minimal training samples while preserving the knowledge of …
classes into a model with minimal training samples while preserving the knowledge of …
Calibrating Higher-Order Statistics for Few-Shot Class-Incremental Learning with Pre-trained Vision Transformers
D Goswami, B Twardowski… - Proceedings of the …, 2024 - openaccess.thecvf.com
Few-shot class-incremental learning (FSCIL) aims to adapt the model to new classes from
very few data (5 samples) without forgetting the previously learned classes. Recent works in …
very few data (5 samples) without forgetting the previously learned classes. Recent works in …
Towards Few-Shot Learning in the Open World: A Review and Beyond
Human intelligence is characterized by our ability to absorb and apply knowledge from the
world around us, especially in rapidly acquiring new concepts from minimal examples …
world around us, especially in rapidly acquiring new concepts from minimal examples …
Few-shot class incremental learning via prompt transfer and knowledge distillation
The ability of a model to learn incrementally from very limited data while still retaining
knowledge about previously seen classes is called few-shot incremental learning. The …
knowledge about previously seen classes is called few-shot incremental learning. The …