Efficient Single-Step Framework for Incremental Class Learning in Neural Networks

• URL: http://arxiv.org/abs/2509.11285v1
• Date: Sun, 14 Sep 2025 14:24:41 GMT
• Title: Efficient Single-Step Framework for Incremental Class Learning in Neural Networks
• Authors: Alejandro Dopico-Castro, Oscar Fontenla-Romero, Bertha Guijarro-Berdiñas, Amparo Alonso-Betanzos,
• Abstract summary: CIFNet (Class Incremental and Frugal Network) is a novel CIL approach that addresses limitations by offering a highly efficient and sustainable solution.<n>A pre-trained and frozen feature extractor eliminates computationally expensive fine-tuning of the backbone.<n> Experiments on benchmark datasets confirm that CIFNet effectively mitigates catastrophic forgetting at the level, achieving high accuracy comparable to that of existing state-of-the-art methods.
• Score: 43.1212452324751
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Incremental learning remains a critical challenge in machine learning, as models often struggle with catastrophic forgetting -the tendency to lose previously acquired knowledge when learning new information. These challenges are even more pronounced in resource-limited settings. Many existing Class Incremental Learning (CIL) methods achieve high accuracy by continually adapting their feature representations; however, they often require substantial computational resources and complex, iterative training procedures. This work introduces CIFNet (Class Incremental and Frugal Network), a novel CIL approach that addresses these limitations by offering a highly efficient and sustainable solution. CIFNet's key innovation lies in its novel integration of several existing, yet separately explored, components: a pre-trained and frozen feature extractor, a compressed data buffer, and an efficient non-iterative one-layer neural network for classification. A pre-trained and frozen feature extractor eliminates computationally expensive fine-tuning of the backbone. This, combined with a compressed buffer for efficient memory use, enables CIFNet to perform efficient class-incremental learning through a single-step optimization process on fixed features, minimizing computational overhead and training time without requiring multiple weight updates. Experiments on benchmark datasets confirm that CIFNet effectively mitigates catastrophic forgetting at the classifier level, achieving high accuracy comparable to that of existing state-of-the-art methods, while substantially improving training efficiency and sustainability. CIFNet represents a significant advancement in making class-incremental learning more accessible and pragmatic in environments with limited resources, especially when strong pre-trained feature extractors are available.

Related papers

• Unlocking Prototype Potential: An Efficient Tuning Framework for Few-Shot Class-Incremental Learning [69.28860905525057]
  Few-shot class-incremental learning (FSCIL) seeks to continuously learn new classes from very limited samples.<n>We introduce an efficient prototype fine-tuning framework that evolves static centroids into dynamic, learnable components.
  arXiv  Detail & Related papers  (2026-02-05T03:50:53Z)
• CAMP-HiVe: Cyclic Pair Merging based Efficient DNN Pruning with Hessian-Vector Approximation for Resource-Constrained Systems [3.343542849202802]
  We introduce CAMP-HiVe, a cyclic pair merging-based pruning with Hessian Vector approximation.<n>Our experimental results demonstrate that our proposed method achieves significant reductions in computational requirements.<n>It outperforms the existing state-of-the-art neural pruning methods.
  arXiv  Detail & Related papers  (2025-11-09T07:58:36Z)
• Forward-Only Continual Learning [8.873948519614244]
  Catastrophic forgetting remains a central challenge in continual learning.<n>We propose FoRo, a forward-only, gradient-free continual learning method.<n>Experiments show that FoRo significantly reduces average forgetting and improves accuracy.
  arXiv  Detail & Related papers  (2025-09-01T15:10:38Z)
• Efficient Machine Unlearning via Influence Approximation [75.31015485113993]
  Influence-based unlearning has emerged as a prominent approach to estimate the impact of individual training samples on model parameters without retraining.<n>This paper establishes a theoretical link between memorizing (incremental learning) and forgetting (unlearning)<n>We introduce the Influence Approximation Unlearning algorithm for efficient machine unlearning from the incremental perspective.
  arXiv  Detail & Related papers  (2025-07-31T05:34:27Z)
• AmorLIP: Efficient Language-Image Pretraining via Amortization [52.533088120633785]
  Contrastive Language-Image Pretraining (CLIP) has demonstrated strong zero-shot performance across diverse downstream text-image tasks.<n>We propose AmorLIP, an efficient CLIP pretraining framework that amortizes expensive computations involved in contrastive learning through lightweight neural networks.
  arXiv  Detail & Related papers  (2025-05-25T05:30:37Z)
• A Good Start Matters: Enhancing Continual Learning with Data-Driven Weight Initialization [15.8696301825572]
  Continuously-trained deep neural networks (DNNs) must rapidly learn new concepts while preserving and utilizing prior knowledge.<n>Weights for newly encountered categories are typically randomly, leading to high initial training loss (spikes) and instability.<n>Inspired by Neural Collapse (NC), we propose a weight initialization strategy to improve learning efficiency in CL.
  arXiv  Detail & Related papers  (2025-03-09T01:44:22Z)
• Center-Sensitive Kernel Optimization for Efficient On-Device Incremental Learning [88.78080749909665]
  Current on-device training methods just focus on efficient training without considering the catastrophic forgetting.<n>This paper proposes a simple but effective edge-friendly incremental learning framework.<n>Our method achieves average accuracy boost of 38.08% with even less memory and approximate computation.
  arXiv  Detail & Related papers  (2024-06-13T05:49:29Z)
• Complementary Learning Subnetworks for Parameter-Efficient Class-Incremental Learning [40.13416912075668]
  We propose a rehearsal-free CIL approach that learns continually via the synergy between two Complementary Learning Subnetworks. Our method achieves competitive results against state-of-the-art methods, especially in accuracy gain, memory cost, training efficiency, and task-order.
  arXiv  Detail & Related papers  (2023-06-21T01:43:25Z)
• Learning in Feedback-driven Recurrent Spiking Neural Networks using full-FORCE Training [4.124948554183487]
  We propose a supervised training procedure for RSNNs, where a second network is introduced only during the training. The proposed training procedure consists of generating targets for both recurrent and readout layers. We demonstrate the improved performance and noise robustness of the proposed full-FORCE training procedure to model 8 dynamical systems.
  arXiv  Detail & Related papers  (2022-05-26T19:01:19Z)
• Incremental Embedding Learning via Zero-Shot Translation [65.94349068508863]
  Current state-of-the-art incremental learning methods tackle catastrophic forgetting problem in traditional classification networks. We propose a novel class-incremental method for embedding network, named as zero-shot translation class-incremental method (ZSTCI) In addition, ZSTCI can easily be combined with existing regularization-based incremental learning methods to further improve performance of embedding networks.
  arXiv  Detail & Related papers  (2020-12-31T08:21:37Z)
• Rapid Structural Pruning of Neural Networks with Set-based Task-Adaptive Meta-Pruning [83.59005356327103]
  A common limitation of most existing pruning techniques is that they require pre-training of the network at least once before pruning. We propose STAMP, which task-adaptively prunes a network pretrained on a large reference dataset by generating a pruning mask on it as a function of the target dataset. We validate STAMP against recent advanced pruning methods on benchmark datasets.
  arXiv  Detail & Related papers  (2020-06-22T10:57:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.