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 …
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 …
Continual learning: Applications and the road forward
Continual learning is a subfield of machine learning, which aims to allow machine learning
models to continuously learn on new data, by accumulating knowledge without forgetting …
models to continuously learn on new data, by accumulating knowledge without forgetting …
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 …
Clip with generative latent replay: a strong baseline for incremental learning
With the emergence of Transformers and Vision-Language Models (VLMs) such as CLIP,
fine-tuning large pre-trained models has recently become a prevalent strategy in Continual …
fine-tuning large pre-trained models has recently become a prevalent strategy in Continual …
Pre-trained Vision and Language Transformers Are Few-Shot Incremental Learners
Abstract Few-Shot Class Incremental Learning (FSCIL) is a task that requires a model to
learn new classes incrementally without forgetting when only a few samples for each class …
learn new classes incrementally without forgetting when only a few samples for each class …
Preventing catastrophic forgetting through memory networks in continuous detection
Modern pre-trained architectures struggle to retain previous information while undergoing
continuous fine-tuning on new tasks. Despite notable progress in continual classification …
continuous fine-tuning on new tasks. Despite notable progress in continual classification …
EvolveDetector: Towards an evolving fake news detector for emerging events with continual knowledge accumulation and transfer
The prevalence of fake news on social media poses devastating and wide-ranging threats to
political beliefs, economic activities, and public health. Due to the continuous emergence of …
political beliefs, economic activities, and public health. Due to the continuous emergence of …
Entropy is not enough for test-time adaptation: From the perspective of disentangled factors
Test-time adaptation (TTA) fine-tunes pre-trained deep neural networks for unseen test data.
The primary challenge of TTA is limited access to the entire test dataset during online …
The primary challenge of TTA is limited access to the entire test dataset during online …