class-incremental learning

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

1 dataset whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 281 papers with code (617 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Overcoming catastrophic forgetting in neural networks
  2 Dec 2016 29 repositories listed Syntology ran 14 of 22 samples · 8 unverified · 4 pointer-only (licence)
  The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence.
• iCaRL: Incremental Classifier and Representation Learning
  23 Nov 2016 10 repositories listed Syntology ran 3 of 14 samples · 11 unverified · 3 pointer-only (licence)
  A major open problem on the road to artificial intelligence is the development of incrementally learning systems that learn about more and more concepts over time from a stream of data.
• Three scenarios for continual learning
  15 Apr 2019 8 repositories listed Syntology ran 0 of 2 samples · 2 unverified
  Standard artificial neural networks suffer from the well-known issue of catastrophic forgetting, making continual or lifelong learning difficult for machine learning.
• Learning to Prompt for Continual Learning
  16 Dec 2021 5 repositories listed Syntology ran 10 of 11 samples · 1 unverified · 10 pointer-only (licence)
  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.
• Rehearsal-Free Continual Learning over Small Non-I.I.D. Batches
  8 Jul 2019 5 repositories listed
  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.
• Large Scale Incremental Learning
  30 May 2019 5 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  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.
• Gradient based sample selection for online continual learning
  20 Mar 2019 5 repositories listed
  To prevent forgetting, a replay buffer is usually employed to store the previous data for the purpose of rehearsal.
• Continual Learning with Deep Generative Replay
  24 May 2017 5 repositories listed
  Attempts to train a comprehensive artificial intelligence capable of solving multiple tasks have been impeded by a chronic problem called catastrophic forgetting.
• RMM: Reinforced Memory Management for Class-Incremental Learning
  14 Jan 2023 4 repositories listed Syntology ran 2 of 4 samples · 2 unverified · 4 pointer-only (licence)
  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.
• Avalanche: an End-to-End Library for Continual Learning
  1 Apr 2021 4 repositories listed Syntology ran 3 of 14 samples · 11 unverified
  Learning continually from non-stationary data streams is a long-standing goal and a challenging problem in machine learning.
• A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
  10 Jul 2018 4 repositories listed Syntology ran 9 of 13 samples · 4 unverified · 12 pointer-only (licence)
  Detecting test samples drawn sufficiently far away from the training distribution statistically or adversarially is a fundamental requirement for deploying a good classifier in many real-world machine learning…
• External Knowledge Injection for CLIP-Based Class-Incremental Learning
  11 Mar 2025 3 repositories listed
  In this paper, we introduce ExterNal knowledGe INjEction (ENGINE) for CLIP-based CIL.
• FeCAM: Exploiting the Heterogeneity of Class Distributions in Exemplar-Free Continual Learning
  25 Sep 2023 3 repositories listed Syntology ran 11 of 21 samples · 10 unverified
  However, when learning from non-stationary data, we observe that the Euclidean metric is suboptimal and that feature distributions are heterogeneous.
• Revisiting Class-Incremental Learning with Pre-Trained Models: Generalizability and Adaptivity are All You Need
  13 Mar 2023 3 repositories listed
  In this work, we revisit CIL with PTMs and argue that the core factors in CIL are adaptivity for model updating and generalizability for knowledge transferring.
• Class-Incremental Learning: A Survey
  7 Feb 2023 3 repositories listed
  Deep models, e.
• A Multi-Head Model for Continual Learning via Out-of-Distribution Replay
  20 Aug 2022 3 repositories listed
  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…
• Supervised Contrastive Replay: Revisiting the Nearest Class Mean Classifier in Online Class-Incremental Continual Learning
  22 Mar 2021 3 repositories listed
  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.
• GDumb: A Simple Approach that Questions Our Progress in Continual Learning
  1 Aug 2020 3 repositories listed
  We discuss a general formulation for the Continual Learning (CL) problem for classification---a learning task where a stream provides samples to a learner and the goal of the learner, depending on the samples it…
• Dark Experience for General Continual Learning: a Strong, Simple Baseline
  15 Apr 2020 3 repositories listed
  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…
• Efficient Lifelong Learning with A-GEM
  2 Dec 2018 3 repositories listed Syntology ran 1 of 12 samples · 11 unverified
  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.
• Continuous Knowledge-Preserving Decomposition for Few-Shot Continual Learning
  9 Jan 2025 2 repositories listed
  To this end, we propose Continuous Knowledge-Preserving Decomposition for FSCIL (CKPD-FSCIL), a framework that decomposes a model's weights into two parts: one that compacts existing knowledge (knowledge-sensitive…
• MOS: Model Surgery for Pre-Trained Model-Based Class-Incremental Learning
  12 Dec 2024 2 repositories listed Syntology ran 6 of 17 samples · 11 unverified · 12 pointer-only (licence)
  Class-Incremental Learning (CIL) requires models to continually acquire knowledge of new classes without forgetting old ones.
• AIR: Analytic Imbalance Rectifier for Continual Learning
  19 Aug 2024 2 repositories listed
  To solve this problem, we propose an analytic imbalance rectifier algorithm (AIR), a novel online exemplar-free continual learning method with an analytic (i.
• Data-Free Generative Replay for Class-Incremental Learning on Imbalanced Data
  7 Jun 2024 2 repositories listed
  One method for incremental class learning, addressing dataset imbalance, is rehearsal using previously stored data.
• GACL: Exemplar-Free Generalized Analytic Continual Learning
  23 Mar 2024 2 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  The GACL adopts analytic learning (a gradient-free training technique) and delivers an analytical (i.
• A streamlined Approach to Multimodal Few-Shot Class Incremental Learning for Fine-Grained Datasets
  10 Mar 2024 2 repositories listed
  Few-shot Class-Incremental Learning (FSCIL) poses the challenge of retaining prior knowledge while learning from limited new data streams, all without overfitting.
• Class incremental learning with probability dampening and cascaded gated classifier
  2 Feb 2024 2 repositories listed
  The latter is a gated incremental classifier, helping the model modify past predictions without directly interfering with them.
• Federated Class-Incremental Learning with New-Class Augmented Self-Distillation
  1 Jan 2024 2 repositories listed
  Federated Learning (FL) enables collaborative model training among participants while guaranteeing the privacy of raw data.
• FILP-3D: Enhancing 3D Few-shot Class-incremental Learning with Pre-trained Vision-Language Models
  28 Dec 2023 2 repositories listed
  To address the above challenges, we introduce the FILP-3D framework with two novel components: the Redundant Feature Eliminator (RFE) for feature space misalignment and the Spatial Noise Compensator (SNC) for…
• Continual Learning: Forget-free Winning Subnetworks for Video Representations
  19 Dec 2023 2 repositories listed Syntology ran 7 of 15 samples · 8 unverified
  Inspired by the Lottery Ticket Hypothesis (LTH), which highlights the existence of efficient subnetworks within larger, dense networks, a high-performing Winning Subnetwork (WSN) in terms of task performance under…

Syntology lines on 13 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.