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Found 49 papers, 28 papers with code
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 Policy Distillation of Reinforcement Learning-based Controllers for Soft Robotic In-Hand Manipulation
The framework leverages Policy Distillation (PD) to transfer knowledge from expert policies to a continually evolving student policy network.
Adaptive Hyperparameter Optimization for Continual Learning Scenarios
This paper aims to explore the role of hyperparameter selection in continual learning and the necessity of continually and automatically tuning them according to the complexity of the task at hand.
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.
Design Principles for Lifelong Learning AI Accelerators
Lifelong learning - an agent's ability to learn throughout its lifetime - is a hallmark of biological learning systems and a central challenge for artificial intelligence (AI).
In-context Interference in Chat-based Large Language Models
However, one limitation of this scenario is that users cannot modify the internal knowledge of the model, and the only way to add or modify internal knowledge is by explicitly mentioning it to the model during the current interaction.
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.
LuckyMera: a Modular AI Framework for Building Hybrid NetHack Agents
In the last few decades we have witnessed a significant development in Artificial Intelligence (AI) thanks to the availability of a variety of testbeds, mostly based on simulated environments and video games.
Partial Hypernetworks for Continual Learning
Inspired by latent replay methods in CL, we propose partial weight generation for the final layers of a model using hypernetworks while freezing the initial layers.
Studying Generalization on Memory-Based Methods in Continual Learning
One of the objectives of Continual Learning is to learn new concepts continually over a stream of experiences and at the same time avoid catastrophic forgetting.
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.
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.
Continual Learning for Predictive Maintenance: Overview and Challenges
In this work, we present a brief introduction to predictive maintenance, non-stationary environments, and continual learning, together with an extensive review of the current state of applying continual learning in real-world applications and specifically in predictive maintenance.
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.
Architect, Regularize and Replay (ARR): a Flexible Hybrid Approach for Continual Learning
In recent years we have witnessed a renewed interest in machine learning methodologies, especially for deep representation learning, that could overcome basic i. i. d.
3rd Continual Learning Workshop Challenge on Egocentric Category and Instance Level Object Understanding
Continual Learning, also known as Lifelong or Incremental Learning, has recently gained renewed interest among the Artificial Intelligence research community.
Memory Population in Continual Learning via Outlier Elimination
This paper introduces Memory Outlier Elimination (MOE), a method for identifying and eliminating outliers in the memory buffer by choosing samples from label-homogeneous subpopulations.
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-as-a-Service (CLaaS): On-Demand Efficient Adaptation of Predictive Models
The two main future trends for companies that want to build machine learning-based applications and systems are real-time inference and continual updating.
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.
Generative Negative Replay for Continual Learning
Learning continually is a key aspect of intelligence and a necessary ability to solve many real-life problems.
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.
Avalanche RL: a Continual Reinforcement Learning Library
Continual Reinforcement Learning (CRL) is a challenging setting where an agent learns to interact with an environment that is constantly changing over time (the stream of experiences).
AI-as-a-Service Toolkit for Human-Centered Intelligence in Autonomous Driving
This paper presents a proof-of-concept implementation of the AI-as-a-Service toolkit developed within the H2020 TEACHING project and designed to implement an autonomous driving personalization system according to the output of an automatic driver's stress recognition algorithm, both of them realizing a Cyber-Physical System of Systems.
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.
Is Class-Incremental Enough for Continual Learning?
The ability of a model to learn continually can be empirically assessed in different continual learning scenarios.
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.
International Workshop on Continual Semi-Supervised Learning: Introduction, Benchmarks and Baselines
The aim of this paper is to formalize a new continual semi-supervised learning (CSSL) paradigm, proposed to the attention of the machine learning community via the IJCAI 2021 International Workshop on Continual Semi-Supervised Learning (CSSL-IJCAI), with the aim of raising field awareness about this problem and mobilizing its effort in this direction.
TEACHING -- Trustworthy autonomous cyber-physical applications through human-centred intelligence
This paper discusses the perspective of the H2020 TEACHING project on the next generation of autonomous applications running in a distributed and highly heterogeneous environment comprising both virtual and physical resources spanning the edge-cloud continuum.
Continual Learning at the Edge: Real-Time Training on Smartphone Devices
On-device training for personalized learning is a challenging research problem.
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.
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.
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.
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.
CVPR 2020 Continual Learning in Computer Vision Competition: Approaches, Results, Current Challenges and Future Directions
In the last few years, we have witnessed a renewed and fast-growing interest in continual learning with deep neural networks with the shared objective of making current AI systems more adaptive, efficient and autonomous.
IROS 2019 Lifelong Robotic Vision Challenge -- Lifelong Object Recognition Report
This report summarizes IROS 2019-Lifelong Robotic Vision Competition (Lifelong Object Recognition Challenge) with methods and results from the top $8$ finalists (out of over~$150$ teams).
Online Continual Learning on Sequences
Online continual learning (OCL) refers to the ability of a system to learn over time from a continuous stream of data without having to revisit previously encountered training samples.
Latent Replay for Real-Time Continual Learning
Continual learning techniques, where complex models are incrementally trained on small batches of new data, can make the learning problem tractable even for CPU-only embedded devices enabling remarkable levels of adaptiveness and autonomy.
OpenLORIS-Object: A Robotic Vision Dataset and Benchmark for Lifelong Deep Learning
Yet, robotic vision poses unique challenges for applying visual algorithms developed from these standard computer vision datasets due to their implicit assumption over non-varying distributions for a fixed set of tasks.
Efficient Continual Learning in Neural Networks with Embedding Regularization
Continual learning of deep neural networks is a key requirement for scaling them up to more complex applicative scenarios and for achieving real lifelong learning of these architectures.
Rehearsal-Free Continual Learning over Small Non-I.I.D. Batches
Ideally, continual learning should be triggered by the availability of short videos of single objects and performed on-line on on-board hardware with fine-grained updates.
Continual Learning for Robotics: Definition, Framework, Learning Strategies, Opportunities and Challenges
An important challenge for machine learning is not necessarily finding solutions that work in the real world but rather finding stable algorithms that can learn in real world.
Continual Reinforcement Learning in 3D Non-stationary Environments
High-dimensional always-changing environments constitute a hard challenge for current reinforcement learning techniques.
Intelligent Drone Swarm for Search and Rescue Operations at Sea
In recent years, a rising numbers of people arrived in the European Union, traveling across the Mediterranean Sea or overland through Southeast Europe in what has been later named as the European migrant crisis.
Don't forget, there is more than forgetting: new metrics for Continual Learning
Continual learning consists of algorithms that learn from a stream of data/tasks continuously and adaptively thought time, enabling the incremental development of ever more complex knowledge and skills.
Custom Dual Transportation Mode Detection by Smartphone Devices Exploiting Sensor Diversity
Making applications aware of the mobility experienced by the user can open the door to a wide range of novel services in different use-cases, from smart parking to vehicular traffic monitoring.
Continuous Learning in Single-Incremental-Task Scenarios
It was recently shown that architectural, regularization and rehearsal strategies can be used to train deep models sequentially on a number of disjoint tasks without forgetting previously acquired knowledge.
CORe50: a New Dataset and Benchmark for Continuous Object Recognition
Continuous/Lifelong learning of high-dimensional data streams is a challenging research problem.
Semi-supervised Tuning from Temporal Coherence
Recent works demonstrated the usefulness of temporal coherence to regularize supervised training or to learn invariant features with deep architectures.
