Incremental Learning
385 papers with code • 22 benchmarks • 9 datasets
Incremental learning aims to develop artificially intelligent systems that can continuously learn to address new tasks from new data while preserving knowledge learned from previously learned tasks.
Benchmarks
These leaderboards are used to track progress in Incremental Learning
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Libraries
Use these libraries to find Incremental Learning models and implementationsDatasets
CIFAR-100
Permuted MNIST
CORe50
INRIA Aerial Image Labeling
TopLogo-10
Concept-1K
HOWS
Latest papers with no code
Tendency-driven Mutual Exclusivity for Weakly Supervised Incremental Semantic Segmentation
However, a scenario usually arises where a pixel is concurrently predicted as an old class by the pre-trained segmentation model and a new class by the seed areas.
Graph Continual Learning with Debiased Lossless Memory Replay
Graph continual learning (GCL) tackles this problem by continually adapting GNNs to the expanded graph of the current task while maintaining the performance over the graph of previous tasks.
Realistic Continual Learning Approach using Pre-trained Models
Continual learning (CL) is crucial for evaluating adaptability in learning solutions to retain knowledge.
Toward industrial use of continual learning : new metrics proposal for class incremental learning
In this paper, we investigate continual learning performance metrics used in class incremental learning strategies for continual learning (CL) using some high performing methods.
Multi-Label Continual Learning for the Medical Domain: A Novel Benchmark
This method aims to mitigate forgetting while adapting to new classes and domain shifts by combining the advantages of the Replay and Pseudo-Label methods and solving their limitations in the proposed scenario.
Liquid Neural Network-based Adaptive Learning vs. Incremental Learning for Link Load Prediction amid Concept Drift due to Network Failures
In this work, we address this challenge for the problem of traffic forecasting and propose an approach that exploits adaptive learning algorithms, namely, liquid neural networks, which are capable of self-adaptation to abrupt changes in data patterns without requiring any retraining.
Future-Proofing Class Incremental Learning
Exemplar-Free Class Incremental Learning is a highly challenging setting where replay memory is unavailable.
Incremental Learning with Concept Drift Detection and Prototype-based Embeddings for Graph Stream Classification
Data stream mining aims at extracting meaningful knowledge from continually evolving data streams, addressing the challenges posed by nonstationary environments, particularly, concept drift which refers to a change in the underlying data distribution over time.
Slightly Shift New Classes to Remember Old Classes for Video Class-Incremental Learning
So we propose SNRO, which slightly shifts the features of new classes to remember old classes.
Semantically-Shifted Incremental Adapter-Tuning is A Continual ViTransformer
We observe that adapter tuning demonstrates superiority over prompt-based methods, even without parameter expansion in each learning session.
