Class Incremental Learning
208 papers with code • 6 benchmarks • 1 datasets
Incremental learning of a sequence of tasks when the task-ID is not available at test time.
Benchmarks
These leaderboards are used to track progress in Class Incremental Learning
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Libraries
Use these libraries to find Class Incremental Learning models and implementations
CIFAR-100
Subtasks
Most implemented papers
Large Scale Incremental Learning
ContinualAI/avalanche
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CVPR 2019
We believe this is because of the combination of two factors: (a) the data imbalance between the old and new classes, and (b) the increasing number of visually similar classes.
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.
Learning to Prompt for Continual Learning
google-research/l2p
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CVPR 2022
The mainstream paradigm behind continual learning has been to adapt the model parameters to non-stationary data distributions, where catastrophic forgetting is the central challenge.
Dark Experience for General Continual Learning: a Strong, Simple Baseline
aimagelab/mammoth
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NeurIPS 2020
Continual Learning has inspired a plethora of approaches and evaluation settings; however, the majority of them overlooks the properties of a practical scenario, where the data stream cannot be shaped as a sequence of tasks and offline training is not viable.
Supervised Contrastive Replay: Revisiting the Nearest Class Mean Classifier in Online Class-Incremental Continual Learning
RaptorMai/online-continual-learning
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Online class-incremental continual learning (CL) studies the problem of learning new classes continually from an online non-stationary data stream, intending to adapt to new data while mitigating catastrophic forgetting.
New Insights on Reducing Abrupt Representation Change in Online Continual Learning
aimagelab/mammoth
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ICLR 2022
In this work, we focus on the change in representations of observed data that arises when previously unobserved classes appear in the incoming data stream, and new classes must be distinguished from previous ones.
A Multi-Head Model for Continual Learning via Out-of-Distribution Replay
k-gyuhak/more
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Instead of using the saved samples in memory to update the network for previous tasks/classes in the existing approach, MORE leverages the saved samples to build a task specific classifier (adding a new classification head) without updating the network learned for previous tasks/classes.
RMM: Reinforced Memory Management for Class-Incremental Learning
Class-Incremental Learning (CIL) [40] trains classifiers under a strict memory budget: in each incremental phase, learning is done for new data, most of which is abandoned to free space for the next phase.
Efficient Lifelong Learning with A-GEM
facebookresearch/agem
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ICLR 2019
In lifelong learning, the learner is presented with a sequence of tasks, incrementally building a data-driven prior which may be leveraged to speed up learning of a new task.
Class-incremental Learning via Deep Model Consolidation
mmasana/FACIL
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The idea is to first train a separate model only for the new classes, and then combine the two individual models trained on data of two distinct set of classes (old classes and new classes) via a novel double distillation training objective.
