Just Say the Name: Online Continual Learning with Category Names Only via Data Generation
- URL: http://arxiv.org/abs/2403.10853v2
- Date: Tue, 30 Apr 2024 15:20:54 GMT
- Title: Just Say the Name: Online Continual Learning with Category Names Only via Data Generation
- Authors: Minhyuk Seo, Diganta Misra, Seongwon Cho, Minjae Lee, Jonghyun Choi,
- Abstract summary: We present an online continual learning framework - Generative Name only Continual Learning (G-NoCL)
G-NoCL employs the novel sample complexity-guided data ensembling technique DIverSity and COmplexity enhancing ensemBlER (DISCOBER) to optimally sample training data from generated data.
- Score: 15.163200258819712
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: In real-world scenarios, extensive manual annotation for continual learning is impractical due to prohibitive costs. Although prior arts, influenced by large-scale webly supervised training, suggest leveraging web-scraped data in continual learning, this poses challenges such as data imbalance, usage restrictions, and privacy concerns. Addressing the risks of continual webly supervised training, we present an online continual learning framework - Generative Name only Continual Learning (G-NoCL). The proposed G-NoCL uses a set of generators G along with the learner. When encountering new concepts (i.e., classes), G-NoCL employs the novel sample complexity-guided data ensembling technique DIverSity and COmplexity enhancing ensemBlER (DISCOBER) to optimally sample training data from generated data. Through extensive experimentation, we demonstrate superior performance of DISCOBER in G-NoCL online CL benchmarks, covering both In-Distribution (ID) and Out-of-Distribution (OOD) generalization evaluations, compared to naive generator-ensembling, web-supervised, and manually annotated data.
Related papers
- Generalization Beyond Data Imbalance: A Controlled Study on CLIP for Transferable Insights [67.72413262980272]
Severe data imbalance naturally exists among web-scale vision-language datasets.
We find CLIP pre-trained thereupon exhibits notable robustness to the data imbalance compared to supervised learning.
The robustness and discriminability of CLIP improve with more descriptive language supervision, larger data scale, and broader open-world concepts.
arXiv Detail & Related papers (2024-05-31T17:57:24Z) - Dynamic Sub-graph Distillation for Robust Semi-supervised Continual
Learning [52.046037471678005]
We focus on semi-supervised continual learning (SSCL), where the model progressively learns from partially labeled data with unknown categories.
We propose a novel approach called Dynamic Sub-Graph Distillation (DSGD) for semi-supervised continual learning.
arXiv Detail & Related papers (2023-12-27T04:40:12Z) - Density Distribution-based Learning Framework for Addressing Online
Continual Learning Challenges [4.715630709185073]
We introduce a density distribution-based learning framework for online Continual Learning.
Our framework achieves superior average accuracy and time-space efficiency.
Our method outperforms popular CL approaches by a significant margin.
arXiv Detail & Related papers (2023-11-22T09:21:28Z) - From Categories to Classifier: Name-Only Continual Learning by Exploring
the Web [125.75085825742092]
Continual learning often relies on the availability of extensive annotated datasets, an assumption that is unrealistically time-consuming and costly in practice.
We explore a novel paradigm termed name-only continual learning where time and cost constraints prohibit manual annotation.
Our proposed solution leverages the expansive and ever-evolving internet to query and download uncurated webly-supervised data for image classification.
arXiv Detail & Related papers (2023-11-19T10:43:43Z) - Beyond Supervised Continual Learning: a Review [69.9674326582747]
Continual Learning (CL) is a flavor of machine learning where the usual assumption of stationary data distribution is relaxed or omitted.
Changes in the data distribution can cause the so-called catastrophic forgetting (CF) effect: an abrupt loss of previous knowledge.
This article reviews literature that study CL in other settings, such as learning with reduced supervision, fully unsupervised learning, and reinforcement learning.
arXiv Detail & Related papers (2022-08-30T14:44:41Z) - Weakly Supervised Continual Learning [17.90483695137098]
This work explores Weakly Supervised Continual Learning (WSCL)
We show that not only our proposals exhibit higher flexibility when supervised information is scarce, but also that less than 25% labels can be enough to reach or even outperform SOTA methods trained under full supervision.
In doing so, we show that not only our proposals exhibit higher flexibility when supervised information is scarce, but also that less than 25% labels can be enough to reach or even outperform SOTA methods trained under full supervision.
arXiv Detail & Related papers (2021-08-14T14:38:20Z) - Regularizing Generative Adversarial Networks under Limited Data [88.57330330305535]
This work proposes a regularization approach for training robust GAN models on limited data.
We show a connection between the regularized loss and an f-divergence called LeCam-divergence, which we find is more robust under limited training data.
arXiv Detail & Related papers (2021-04-07T17:59:06Z) - ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for
Semi-supervised Continual Learning [52.831894583501395]
Continual learning assumes the incoming data are fully labeled, which might not be applicable in real applications.
We propose deep Online Replay with Discriminator Consistency (ORDisCo) to interdependently learn a classifier with a conditional generative adversarial network (GAN)
We show ORDisCo achieves significant performance improvement on various semi-supervised learning benchmark datasets for SSCL.
arXiv Detail & Related papers (2021-01-02T09:04:14Z) - Few-Shot Unsupervised Continual Learning through Meta-Examples [21.954394608030388]
We introduce a novel and complex setting involving unsupervised meta-continual learning with unbalanced tasks.
We exploit a meta-learning scheme that simultaneously alleviates catastrophic forgetting and favors the generalization to new tasks.
Experimental results on few-shot learning benchmarks show competitive performance even compared to the supervised case.
arXiv Detail & Related papers (2020-09-17T07:02:07Z)
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.