ACIL: Active Class Incremental Learning for Image Classification

• URL: http://arxiv.org/abs/2602.04252v1
• Date: Wed, 04 Feb 2026 06:22:30 GMT
• Title: ACIL: Active Class Incremental Learning for Image Classification
• Authors: Aditya R. Bhattacharya, Debanjan Goswami, Shayok Chakraborty,
• Abstract summary: We propose ACIL, a novel active learning framework for class incremental learning settings.<n>We exploit a criterion based on uncertainty and diversity to identify the samples that need to be annotated in each episode.
• Score: 4.779196219827506
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Continual learning (or class incremental learning) is a realistic learning scenario for computer vision systems, where deep neural networks are trained on episodic data, and the data from previous episodes are generally inaccessible to the model. Existing research in this domain has primarily focused on avoiding catastrophic forgetting, which occurs due to the continuously changing class distributions in each episode and the inaccessibility of the data from previous episodes. However, these methods assume that all the training samples in every episode are annotated; this not only incurs a huge annotation cost, but also results in a wastage of annotation effort, since most of the samples in a given episode will not be accessible to the model in subsequent episodes. Active learning algorithms identify the salient and informative samples from large amounts of unlabeled data and are instrumental in reducing the human annotation effort in inducing a deep neural network. In this paper, we propose ACIL, a novel active learning framework for class incremental learning settings. We exploit a criterion based on uncertainty and diversity to identify the exemplar samples that need to be annotated in each episode, and will be appended to the data in the next episode. Such a framework can drastically reduce annotation cost and can also avoid catastrophic forgetting. Our extensive empirical analyses on several vision datasets corroborate the promise and potential of our framework against relevant baselines.

Related papers

• Learning from Neighbors: Category Extrapolation for Long-Tail Learning [62.30734737735273]
  We offer a novel perspective on long-tail learning, inspired by an observation: datasets with finer granularity tend to be less affected by data imbalance.<n>We introduce open-set auxiliary classes that are visually similar to existing ones, aiming to enhance representation learning for both head and tail classes.<n>To prevent the overwhelming presence of auxiliary classes from disrupting training, we introduce a neighbor-silencing loss.
  arXiv  Detail & Related papers  (2024-10-21T13:06:21Z)
• Enhancing Consistency and Mitigating Bias: A Data Replay Approach for Incremental Learning [93.90047628101155]
  Deep learning systems are prone to catastrophic forgetting when learning from a sequence of tasks.<n>To address this, some methods propose replaying data from previous tasks during new task learning.<n>However, it is not expected in practice due to memory constraints and data privacy issues.
  arXiv  Detail & Related papers  (2024-01-12T12:51:12Z)
• Contrastive Deep Encoding Enables Uncertainty-aware Machine-learning-assisted Histopathology [6.548275341067594]
  terabytes of training data can be consciously utilized to pre-train deep networks to encode informative representations. We show that our approach can reach the state-of-the-art (SOTA) for patch-level classification with only 1-10% randomly selected annotations.
  arXiv  Detail & Related papers  (2023-09-13T17:37:19Z)
• Learning to Unlearn: Instance-wise Unlearning for Pre-trained Classifiers [71.70205894168039]
  We consider instance-wise unlearning, of which the goal is to delete information on a set of instances from a pre-trained model. We propose two methods that reduce forgetting on the remaining data: 1) utilizing adversarial examples to overcome forgetting at the representation-level and 2) leveraging weight importance metrics to pinpoint network parameters guilty of propagating unwanted information.
  arXiv  Detail & Related papers  (2023-01-27T07:53:50Z)
• Class Impression for Data-free Incremental Learning [20.23329169244367]
  Deep learning-based classification approaches require collecting all samples from all classes in advance and are trained offline. This paradigm may not be practical in real-world clinical applications, where new classes are incrementally introduced through the addition of new data. We propose a novel data-free class incremental learning framework that first synthesizes data from the model trained on previous classes to generate a ours.
  arXiv  Detail & Related papers  (2022-06-26T06:20:17Z)
• Learning Debiased and Disentangled Representations for Semantic Segmentation [52.35766945827972]
  We propose a model-agnostic and training scheme for semantic segmentation. By randomly eliminating certain class information in each training iteration, we effectively reduce feature dependencies among classes. Models trained with our approach demonstrate strong results on multiple semantic segmentation benchmarks.
  arXiv  Detail & Related papers  (2021-10-31T16:15:09Z)
• Bridging Non Co-occurrence with Unlabeled In-the-wild Data for Incremental Object Detection [56.22467011292147]
  Several incremental learning methods are proposed to mitigate catastrophic forgetting for object detection. Despite the effectiveness, these methods require co-occurrence of the unlabeled base classes in the training data of the novel classes. We propose the use of unlabeled in-the-wild data to bridge the non-occurrence caused by the missing base classes during the training of additional novel classes.
  arXiv  Detail & Related papers  (2021-10-28T10:57:25Z)
• Class-incremental Learning using a Sequence of Partial Implicitly Regularized Classifiers [0.0]
  In class-incremental learning, the objective is to learn a number of classes sequentially without having access to the whole training data. Our experiments on CIFAR100 dataset show that the proposed method improves the performance over SOTA by a large margin.
  arXiv  Detail & Related papers  (2021-04-04T10:02:45Z)
• A generic ensemble based deep convolutional neural network for semi-supervised medical image segmentation [7.141405427125369]
  We propose a generic semi-supervised learning framework for image segmentation based on a deep convolutional neural network (DCNN) Our method is able to significantly improve beyond fully supervised model learning by incorporating unlabeled data.
  arXiv  Detail & Related papers  (2020-04-16T23:41:50Z)
• Confident Coreset for Active Learning in Medical Image Analysis [57.436224561482966]
  We propose a novel active learning method, confident coreset, which considers both uncertainty and distribution for effectively selecting informative samples. By comparative experiments on two medical image analysis tasks, we show that our method outperforms other active learning methods.
  arXiv  Detail & Related papers  (2020-04-05T13:46:16Z)

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.