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Found 8 papers, 6 papers with code
Exemplar-free Continual Representation Learning via Learnable Drift Compensation
Prototype-based approaches, when continually updated, face the critical issue of semantic drift due to which the old class prototypes drift to different positions in the new feature space.
FedFNN: Faster Training Convergence Through Update Predictions in Federated Recommender Systems
Federated Learning (FL) has emerged as a key approach for distributed machine learning, enhancing online personalization while ensuring user data privacy.
Plasticity-Optimized Complementary Networks for Unsupervised Continual Learning
In the second phase, we combine this new knowledge with the previous network in an adaptation-retrospection phase to avoid forgetting and initialize a new expert with the knowledge of the old network.
Exploiting Graph Structured Cross-Domain Representation for Multi-Domain Recommendation
This approach helps to mitigate the negative knowledge transfer problem from multiple domains and improve overall representation.
Continually Learning Self-Supervised Representations with Projected Functional Regularization
Recent self-supervised learning methods are able to learn high-quality image representations and are closing the gap with supervised approaches.
Reducing Label Effort: Self-Supervised meets Active Learning
Our experiments reveal that self-training is remarkably more efficient than active learning at reducing the labeling effort, that for a low labeling budget, active learning offers no benefit to self-training, and finally that the combination of active learning and self-training is fruitful when the labeling budget is high.
Class-incremental learning: survey and performance evaluation on image classification
For future learning systems, incremental learning is desirable because it allows for: efficient resource usage by eliminating the need to retrain from scratch at the arrival of new data; reduced memory usage by preventing or limiting the amount of data required to be stored -- also important when privacy limitations are imposed; and learning that more closely resembles human learning.
Orderless Recurrent Models for Multi-label Classification
Recurrent neural networks (RNN) are popular for many computer vision tasks, including multi-label classification.
