Breaking Fine-Grained Classification Barriers with Cost-Free Data in Few-Shot Class-Incremental Learning

• URL: http://arxiv.org/abs/2412.20383v1
• Date: Sun, 29 Dec 2024 07:11:44 GMT
• Title: Breaking Fine-Grained Classification Barriers with Cost-Free Data in Few-Shot Class-Incremental Learning
• Authors: Li-Jun Zhao, Zhen-Duo Chen, Zhi-Yuan Xue, Xin Luo, Xin-Shun Xu,
• Abstract summary: We propose a novel learning paradigm to break barriers in fine-grained classification.<n>It enables the model to learn beyond the standard training phase and benefit from cost-free data encountered during system operation.
• Score: 13.805180905579832
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current fine-grained classification research mainly concentrates on fine-grained feature learning, but in real-world applications, the bigger issue often lies in the data. Fine-grained data annotation is challenging, and the features and semantics are highly diverse and frequently changing, making traditional methods less effective in real-world scenarios. Although some studies have provided potential solutions to this issue, most are limited to making use of limited supervised information. In this paper, we propose a novel learning paradigm to break barriers in fine-grained classification. It enables the model to learn beyond the standard training phase and benefit from cost-free data encountered during system operation. On this basis, an efficient EXPloring and EXPloiting strategy and method (EXP2) is designed. Thereinto, before the final classification results are obtained, representative inference data samples are explored according to class templates and exploited to optimize classifiers. Experimental results demonstrate the general effectiveness of EXP2.

Related papers

• Preview-based Category Contrastive Learning for Knowledge Distillation [53.551002781828146]
  We propose a novel preview-based category contrastive learning method for knowledge distillation (PCKD) It first distills the structural knowledge of both instance-level feature correspondence and the relation between instance features and category centers. It can explicitly optimize the category representation and explore the distinct correlation between representations of instances and categories.
  arXiv  Detail & Related papers  (2024-10-18T03:31:00Z)
• Exploiting Fine-Grained Prototype Distribution for Boosting Unsupervised Class Incremental Learning [13.17775851211893]
  This paper explores a more challenging problem of unsupervised class incremental learning (UCIL) The essence of addressing this problem lies in effectively capturing comprehensive feature representations and discovering unknown novel classes. We propose a strategy to minimize overlap between novel and existing classes, thereby preserving historical knowledge and mitigating the phenomenon of catastrophic forgetting.
  arXiv  Detail & Related papers  (2024-08-19T14:38:27Z)
• TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time (Extended Version) [18.146377453918724]
  Malware detectors often experience performance decay due to constantly evolving operating systems and attack methods. This paper argues that commonly reported results are inflated due to two pervasive sources of experimental bias in the detection task.
  arXiv  Detail & Related papers  (2024-02-02T12:27:32Z)
• Learning to Learn for Few-shot Continual Active Learning [9.283518682371756]
  Continual learning strives to ensure stability in solving previously seen tasks while demonstrating plasticity in a novel domain. Recent advances in continual learning are mostly confined to a supervised learning setting, especially in NLP domain. We exploit meta-learning and propose a method, called Meta-Continual Active Learning.
  arXiv  Detail & Related papers  (2023-11-07T05:22:11Z)
• Adaptive End-to-End Metric Learning for Zero-Shot Cross-Domain Slot Filling [2.6056468338837457]
  Slot filling poses a critical challenge to handle a novel domain whose samples are never seen during training. Most prior works deal with this problem in a two-pass pipeline manner based on metric learning. We propose a new adaptive end-to-end metric learning scheme for the challenging zero-shot slot filling.
  arXiv  Detail & Related papers  (2023-10-23T19:01:16Z)
• Open World Classification with Adaptive Negative Samples [89.2422451410507]
  Open world classification is a task in natural language processing with key practical relevance and impact. We propose an approach based on underlineadaptive underlinesamples (ANS) designed to generate effective synthetic open category samples in the training stage. ANS achieves significant improvements over state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-03-09T21:12:46Z)
• Systematic Evaluation of Predictive Fairness [60.0947291284978]
  Mitigating bias in training on biased datasets is an important open problem. We examine the performance of various debiasing methods across multiple tasks. We find that data conditions have a strong influence on relative model performance.
  arXiv  Detail & Related papers  (2022-10-17T05:40:13Z)
• Prototype-Anchored Learning for Learning with Imperfect Annotations [83.7763875464011]
  It is challenging to learn unbiased classification models from imperfectly annotated datasets. We propose a prototype-anchored learning (PAL) method, which can be easily incorporated into various learning-based classification schemes. We verify the effectiveness of PAL on class-imbalanced learning and noise-tolerant learning by extensive experiments on synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2022-06-23T10:25:37Z)
• CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning [55.733193075728096]
  Modern deep neural networks can easily overfit to biased training data containing corrupted labels or class imbalance. Sample re-weighting methods are popularly used to alleviate this data bias issue. We propose a meta-model capable of adaptively learning an explicit weighting scheme directly from data.
  arXiv  Detail & Related papers  (2022-02-11T13:49:51Z)
• 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)
• MCDAL: Maximum Classifier Discrepancy for Active Learning [74.73133545019877]
  Recent state-of-the-art active learning methods have mostly leveraged Generative Adversarial Networks (GAN) for sample acquisition. We propose in this paper a novel active learning framework that we call Maximum Discrepancy for Active Learning (MCDAL) In particular, we utilize two auxiliary classification layers that learn tighter decision boundaries by maximizing the discrepancies among them.
  arXiv  Detail & Related papers  (2021-07-23T06:57:08Z)
• Just Label What You Need: Fine-Grained Active Selection for Perception and Prediction through Partially Labeled Scenes [78.23907801786827]
  We introduce generalizations that ensure that our approach is both cost-aware and allows for fine-grained selection of examples through partially labeled scenes. Our experiments on a real-world, large-scale self-driving dataset suggest that fine-grained selection can improve the performance across perception, prediction, and downstream planning tasks.
  arXiv  Detail & Related papers  (2021-04-08T17:57:41Z)
• A Realistic Evaluation of Semi-Supervised Learning for Fine-Grained Classification [38.68079253627819]
  Our benchmark consists of two fine-grained classification datasets obtained by sampling classes from the Aves and Fungi taxonomy. We find that recently proposed SSL methods provide significant benefits, and can effectively use out-of-class data to improve performance when deep networks are trained from scratch. Our work suggests that semi-supervised learning with experts on realistic datasets may require different strategies than those currently prevalent in the literature.
  arXiv  Detail & Related papers  (2021-04-01T17:59:41Z)
• Towards Cross-Granularity Few-Shot Learning: Coarse-to-Fine Pseudo-Labeling with Visual-Semantic Meta-Embedding [13.063136901934865]
  Few-shot learning aims at rapidly adapting to novel categories with only a handful of samples at test time. In this paper, we advance the few-shot classification paradigm towards a more challenging scenario, i.e., cross-granularity few-shot classification. We approximate the fine-grained data distribution by greedy clustering of each coarse-class into pseudo-fine-classes according to the similarity of image embeddings.
  arXiv  Detail & Related papers  (2020-07-11T03:44:21Z)
• Toward Optimal Probabilistic Active Learning Using a Bayesian Approach [4.380488084997317]
  Active learning aims at reducing the labeling costs by an efficient and effective allocation of costly labeling resources. By reformulating existing selection strategies within our proposed model, we can explain which aspects are not covered in current state-of-the-art.
  arXiv  Detail & Related papers  (2020-06-02T15:59:42Z)

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.