Forward compatible few-shot class-incremental learning
Novel classes frequently arise in our dynamically changing world, eg, new users in the
authentication system, and a machine learning model should recognize new classes without …
authentication system, and a machine learning model should recognize new classes without …
Learning with fantasy: Semantic-aware virtual contrastive constraint for few-shot class-incremental learning
Few-shot class-incremental learning (FSCIL) aims at learning to classify new classes
continually from limited samples without forgetting the old classes. The mainstream …
continually from limited samples without forgetting the old classes. The mainstream …
Few-shot incremental learning with continually evolved classifiers
Few-shot class-incremental learning (FSCIL) aims to design machine learning algorithms
that can continually learn new concepts from a few data points, without forgetting knowledge …
that can continually learn new concepts from a few data points, without forgetting knowledge …
Few-shot class-incremental learning from an open-set perspective
The continual appearance of new objects in the visual world poses considerable challenges
for current deep learning methods in real-world deployments. The challenge of new task …
for current deep learning methods in real-world deployments. The challenge of new task …
Few-shot class-incremental learning via class-aware bilateral distillation
Abstract Few-Shot Class-Incremental Learning (FSCIL) aims to continually learn novel
classes based on only few training samples, which poses a more challenging task than the …
classes based on only few training samples, which poses a more challenging task than the …
Metafscil: A meta-learning approach for few-shot class incremental learning
In this paper, we tackle the problem of few-shot class incremental learning (FSCIL). FSCIL
aims to incrementally learn new classes with only a few samples in each class. Most existing …
aims to incrementally learn new classes with only a few samples in each class. Most existing …
S3c: Self-supervised stochastic classifiers for few-shot class-incremental learning
Few-shot class-incremental learning (FSCIL) aims to learn progressively about new classes
with very few labeled samples, without forgetting the knowledge of already learnt classes …
with very few labeled samples, without forgetting the knowledge of already learnt classes …
Few-shot class-incremental learning via training-free prototype calibration
Real-world scenarios are usually accompanied by continuously appearing classes with
scare labeled samples, which require the machine learning model to incrementally learn …
scare labeled samples, which require the machine learning model to incrementally learn …
Self-promoted prototype refinement for few-shot class-incremental learning
Few-shot class-incremental learning is to recognize the new classes given few samples and
not forget the old classes. It is a challenging task since representation optimization and …
not forget the old classes. It is a challenging task since representation optimization and …
Gkeal: Gaussian kernel embedded analytic learning for few-shot class incremental task
Few-shot class incremental learning (FSCIL) aims to address catastrophic forgetting during
class incremental learning in a few-shot learning setting. In this paper, we approach the …
class incremental learning in a few-shot learning setting. In this paper, we approach the …