Search Results for author:
Found 11 papers, 7 papers with code
MultIOD: Rehearsal-free Multihead Incremental Object Detector
The main challenge of class-incremental learning is catastrophic forgetting, the inability of neural networks to retain past knowledge when learning a new one.
PlaStIL: Plastic and Stable Memory-Free Class-Incremental Learning
Mainstream methods need to store two deep models since they integrate new classes using fine-tuning with knowledge distillation from the previous incremental state.
A Comparative Study of Calibration Methods for Imbalanced Class Incremental Learning
Since memory is bounded, old classes are learned with fewer images than new classes and an imbalance due to incremental learning is added to the initial dataset imbalance.
Dataset Knowledge Transfer for Class-Incremental Learning without Memory
We introduce a two-step learning process which allows the transfer of bias correction parameters between reference and target datasets.
Avalanche: an End-to-End Library for Continual Learning
Learning continually from non-stationary data streams is a long-standing goal and a challenging problem in machine learning.
A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks
A second type of approaches fix the deep model size and introduce a mechanism whose objective is to ensure a good compromise between stability and plasticity of the model.
Initial Classifier Weights Replay for Memoryless Class Incremental Learning
It leverages initial classifier weights which provide a strong representation of past classes because they are trained with all class data.
Active Class Incremental Learning for Imbalanced Datasets
Most existing algorithms make two strong hypotheses which reduce the realism of the incremental scenario: (1) new data are assumed to be readily annotated when streamed and (2) tests are run with balanced datasets while most real-life datasets are actually imbalanced.
ScaIL: Classifier Weights Scaling for Class Incremental Learning
The problem is non trivial if the agent runs on a limited computational budget and has a bounded memory of past data.
IL2M: Class Incremental Learning With Dual Memory
This paper presents a class incremental learning (IL) method which exploits fine tuning and a dual memory to reduce the negative effect of catastrophic forgetting in image recognition.
DeeSIL: Deep-Shallow Incremental Learning
Incremental Learning (IL) is an interesting AI problem when the algorithm is assumed to work on a budget.
