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Found 21 papers, 15 papers with code
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.
Avalanche: A PyTorch Library for Deep Continual Learning
Continual learning is the problem of learning from a nonstationary stream of data, a fundamental issue for sustainable and efficient training of deep neural networks over time.
A Comprehensive Empirical Evaluation on Online Continual Learning
Online continual learning aims to get closer to a live learning experience by learning directly on a stream of data with temporally shifting distribution and by storing a minimum amount of data from that stream.
Distilled Replay: Overcoming Forgetting through Synthetic Samples
Replay strategies are Continual Learning techniques which mitigate catastrophic forgetting by keeping a buffer of patterns from previous experiences, which are interleaved with new data during training.
Sample Condensation in Online Continual Learning
A popular solution in these scenario is to use a small memory to retain old data and rehearse them over time.
Calibration of Continual Learning Models
Continual Learning (CL) focuses on maximizing the predictive performance of a model across a non-stationary stream of data.
Continual Pre-Training Mitigates Forgetting in Language and Vision
We formalize and investigate the characteristics of the continual pre-training scenario in both language and vision environments, where a model is continually pre-trained on a stream of incoming data and only later fine-tuned to different downstream tasks.
Continual Learning with Gated Incremental Memories for sequential data processing
The ability to learn in dynamic, nonstationary environments without forgetting previous knowledge, also known as Continual Learning (CL), is a key enabler for scalable and trustworthy deployments of adaptive solutions.
Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph Classification
In this work, we study the phenomenon of catastrophic forgetting in the graph representation learning scenario.
Continual Learning with Echo State Networks
Continual Learning (CL) refers to a learning setup where data is non stationary and the model has to learn without forgetting existing knowledge.
Ex-Model: Continual Learning from a Stream of Trained Models
Learning continually from non-stationary data streams is a challenging research topic of growing popularity in the last few years.
Class-Incremental Learning with Repetition
We propose two stochastic stream generators that produce a wide range of CIR streams starting from a single dataset and a few interpretable control parameters.
A Protocol for Continual Explanation of SHAP
Continual Learning trains models on a stream of data, with the aim of learning new information without forgetting previous knowledge.
Continual Learning for Human State Monitoring
Continual Learning (CL) on time series data represents a promising but under-studied avenue for real-world applications.
Continual Learning for Recurrent Neural Networks: an Empirical Evaluation
We propose two new benchmarks for CL with sequential data based on existing datasets, whose characteristics resemble real-world applications.
Sustainable Artificial Intelligence through Continual Learning
The increasing attention on Artificial Intelligence (AI) regulation has led to the definition of a set of ethical principles grouped into the Sustainable AI framework.
Is Class-Incremental Enough for Continual Learning?
The ability of a model to learn continually can be empirically assessed in different continual learning scenarios.
Practical Recommendations for Replay-based Continual Learning Methods
Continual Learning requires the model to learn from a stream of dynamic, non-stationary data without forgetting previous knowledge.
Projected Latent Distillation for Data-Agnostic Consolidation in Distributed Continual Learning
We formalize this problem as a Distributed Continual Learning scenario, where SCD adapt to local tasks and a CL model consolidates the knowledge from the resulting stream of models without looking at the SCD's private data.
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 what was learned in the past.
MultiSTOP: Solving Functional Equations with Reinforcement Learning
We develop MultiSTOP, a Reinforcement Learning framework for solving functional equations in physics.
