LTRL: Boosting Long-tail Recognition via Reflective Learning

• URL: http://arxiv.org/abs/2407.12568v2
• Date: Fri, 13 Sep 2024 11:31:05 GMT
• Title: LTRL: Boosting Long-tail Recognition via Reflective Learning
• Authors: Qihao Zhao, Yalun Dai, Shen Lin, Wei Hu, Fan Zhang, Jun Liu,
• Abstract summary: We propose a novel learning paradigm, called reflecting learning, in handling long-tail recognition. Our method integrates three processes for reviewing past predictions during training, summarizing and leveraging the feature relation across classes, and correcting gradient conflict for loss functions.
• Score: 12.784186450718652
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In real-world scenarios, where knowledge distributions exhibit long-tail. Humans manage to master knowledge uniformly across imbalanced distributions, a feat attributed to their diligent practices of reviewing, summarizing, and correcting errors. Motivated by this learning process, we propose a novel learning paradigm, called reflecting learning, in handling long-tail recognition. Our method integrates three processes for reviewing past predictions during training, summarizing and leveraging the feature relation across classes, and correcting gradient conflict for loss functions. These designs are lightweight enough to plug and play with existing long-tail learning methods, achieving state-of-the-art performance in popular long-tail visual benchmarks. The experimental results highlight the great potential of reflecting learning in dealing with long-tail recognition.

Related papers

• Granularity Matters in 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. We introduce open-set auxiliary classes that are visually similar to existing ones, aiming to enhance representation learning for both head and tail classes. 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)
• Temporal-Difference Variational Continual Learning [89.32940051152782]
  A crucial capability of Machine Learning models in real-world applications is the ability to continuously learn new tasks. In Continual Learning settings, models often struggle to balance learning new tasks with retaining previous knowledge. We propose new learning objectives that integrate the regularization effects of multiple previous posterior estimations.
  arXiv  Detail & Related papers  (2024-10-10T10:58:41Z)
• A Systematic Review on Long-Tailed Learning [12.122327726952946]
  Long-tailed learning aims to build high-performance models on datasets with long-tailed distributions. We propose a new taxonomy for long-tailed learning, which consists of eight different dimensions. We present a systematic review of long-tailed learning methods, discussing their commonalities and alignable differences.
  arXiv  Detail & Related papers  (2024-08-01T11:39:45Z)
• DELTA: Decoupling Long-Tailed Online Continual Learning [7.507868991415516]
  Long-Tailed Online Continual Learning (LTOCL) aims to learn new tasks from sequentially arriving class-imbalanced data streams. We present DELTA, a decoupled learning approach designed to enhance learning representations. We demonstrate that DELTA improves the capacity for incremental learning, surpassing existing OCL methods.
  arXiv  Detail & Related papers  (2024-04-06T02:33:04Z)
• Contrastive Learning with Boosted Memorization [36.957895270908324]
  Self-supervised learning has achieved a great success in the representation learning of visual and textual data. Recent attempts to consider self-supervised long-tailed learning are made by rebalancing in the loss perspective or the model perspective. We propose a novel Boosted Contrastive Learning (BCL) method to enhance the long-tailed learning in the label-unaware context.
  arXiv  Detail & Related papers  (2022-05-25T11:54:22Z)
• Relational Experience Replay: Continual Learning by Adaptively Tuning Task-wise Relationship [54.73817402934303]
  We propose Experience Continual Replay (ERR), a bi-level learning framework to adaptively tune task-wise to achieve a better stability plasticity' tradeoff. ERR can consistently improve the performance of all baselines and surpass current state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-12-31T12:05:22Z)
• Deep Long-Tailed Learning: A Survey [163.16874896812885]
  Deep long-tailed learning aims to train well-performing deep models from a large number of images that follow a long-tailed class distribution. Long-tailed class imbalance is a common problem in practical visual recognition tasks. This paper provides a comprehensive survey on recent advances in deep long-tailed learning.
  arXiv  Detail & Related papers  (2021-10-09T15:25:22Z)
• Co$^2$L: Contrastive Continual Learning [69.46643497220586]
  Recent breakthroughs in self-supervised learning show that such algorithms learn visual representations that can be transferred better to unseen tasks. We propose a rehearsal-based continual learning algorithm that focuses on continually learning and maintaining transferable representations.
  arXiv  Detail & Related papers  (2021-06-28T06:14:38Z)
• Rehearsal revealed: The limits and merits of revisiting samples in continual learning [43.40531878205344]
  We provide insight into the limits and merits of rehearsal, one of continual learning's most established methods. We show that models trained sequentially with rehearsal tend to stay in the same low-loss region after a task has finished, but are at risk of overfitting on its sample memory.
  arXiv  Detail & Related papers  (2021-04-15T13:28:14Z)
• Learning From Multiple Experts: Self-paced Knowledge Distillation for Long-tailed Classification [106.08067870620218]
  We propose a self-paced knowledge distillation framework, termed Learning From Multiple Experts (LFME) We refer to these models as 'Experts', and the proposed LFME framework aggregates the knowledge from multiple 'Experts' to learn a unified student model. We conduct extensive experiments and demonstrate that our method is able to achieve superior performances compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-01-06T12:57:36Z)

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.