Boosting Few-Shot Text Classification via Distribution Estimation

• URL: http://arxiv.org/abs/2303.16764v1
• Date: Sun, 26 Mar 2023 05:58:39 GMT
• Title: Boosting Few-Shot Text Classification via Distribution Estimation
• Authors: Han Liu, Feng Zhang, Xiaotong Zhang, Siyang Zhao, Fenglong Ma, Xiao-Ming Wu, Hongyang Chen, Hong Yu, Xianchao Zhang
• Abstract summary: We propose two simple yet effective strategies to estimate the distributions of the novel classes by utilizing unlabeled query samples. Specifically, we first assume a class or sample follows the Gaussian distribution, and use the original support set and the nearest few query samples. Then, we augment the labeled samples by sampling from the estimated distribution, which can provide sufficient supervision for training the classification model.
• Score: 38.99459686893034
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Distribution estimation has been demonstrated as one of the most effective approaches in dealing with few-shot image classification, as the low-level patterns and underlying representations can be easily transferred across different tasks in computer vision domain. However, directly applying this approach to few-shot text classification is challenging, since leveraging the statistics of known classes with sufficient samples to calibrate the distributions of novel classes may cause negative effects due to serious category difference in text domain. To alleviate this issue, we propose two simple yet effective strategies to estimate the distributions of the novel classes by utilizing unlabeled query samples, thus avoiding the potential negative transfer issue. Specifically, we first assume a class or sample follows the Gaussian distribution, and use the original support set and the nearest few query samples to estimate the corresponding mean and covariance. Then, we augment the labeled samples by sampling from the estimated distribution, which can provide sufficient supervision for training the classification model. Extensive experiments on eight few-shot text classification datasets show that the proposed method outperforms state-of-the-art baselines significantly.

Related papers

• Liberating Seen Classes: Boosting Few-Shot and Zero-Shot Text Classification via Anchor Generation and Classification Reframing [38.84431954053434]
  Few-shot and zero-shot text classification aim to recognize samples from novel classes with limited labeled samples or no labeled samples at all. We propose a simple and effective strategy for few-shot and zero-shot text classification.
  arXiv  Detail & Related papers  (2024-05-06T15:38:32Z)
• Proposal Distribution Calibration for Few-Shot Object Detection [65.19808035019031]
  In few-shot object detection (FSOD), the two-step training paradigm is widely adopted to mitigate the severe sample imbalance. Unfortunately, the extreme data scarcity aggravates the proposal distribution bias, hindering the RoI head from evolving toward novel classes. We introduce a simple yet effective proposal distribution calibration (PDC) approach to neatly enhance the localization and classification abilities of the RoI head.
  arXiv  Detail & Related papers  (2022-12-15T05:09:11Z)
• Local overlap reduction procedure for dynamic ensemble selection [13.304462985219237]
  Class imbalance is a characteristic known for making learning more challenging for classification models. We propose a DS technique which attempts to minimize the effects of the local class overlap during the classification procedure. Experimental results show that the proposed technique can significantly outperform the baseline.
  arXiv  Detail & Related papers  (2022-06-16T21:31:05Z)
• POODLE: Improving Few-shot Learning via Penalizing Out-of-Distribution Samples [19.311470287767385]
  We propose to use out-of-distribution samples, i.e., unlabeled samples coming from outside the target classes, to improve few-shot learning. Our approach is simple to implement, agnostic to feature extractors, lightweight without any additional cost for pre-training, and applicable to both inductive and transductive settings.
  arXiv  Detail & Related papers  (2022-06-08T18:59:21Z)
• Imbalanced Classification via a Tabular Translation GAN [4.864819846886142]
  We present a model based on Generative Adversarial Networks which uses additional regularization losses to map majority samples to corresponding synthetic minority samples. We show that the proposed method improves average precision when compared to alternative re-weighting and oversampling techniques.
  arXiv  Detail & Related papers  (2022-04-19T06:02:53Z)
• Few-shot Forgery Detection via Guided Adversarial Interpolation [56.59499187594308]
  Existing forgery detection methods suffer from significant performance drops when applied to unseen novel forgery approaches. We propose Guided Adversarial Interpolation (GAI) to overcome the few-shot forgery detection problem. Our method is validated to be robust to choices of majority and minority forgery approaches.
  arXiv  Detail & Related papers  (2022-04-12T16:05:10Z)
• Saliency Grafting: Innocuous Attribution-Guided Mixup with Calibrated Label Mixing [104.630875328668]
  Mixup scheme suggests mixing a pair of samples to create an augmented training sample. We present a novel, yet simple Mixup-variant that captures the best of both worlds.
  arXiv  Detail & Related papers  (2021-12-16T11:27:48Z)
• Free Lunch for Few-shot Learning: Distribution Calibration [10.474018806591397]
  We show that a simple logistic regression classifier trained using the features sampled from our calibrated distribution can outperform the state-of-the-art accuracy on two datasets.
  arXiv  Detail & Related papers  (2021-01-16T07:58:40Z)
• Few-shot Classification via Adaptive Attention [93.06105498633492]
  We propose a novel few-shot learning method via optimizing and fast adapting the query sample representation based on very few reference samples. As demonstrated experimentally, the proposed model achieves state-of-the-art classification results on various benchmark few-shot classification and fine-grained recognition datasets.
  arXiv  Detail & Related papers  (2020-08-06T05:52:59Z)
• M2m: Imbalanced Classification via Major-to-minor Translation [79.09018382489506]
  In most real-world scenarios, labeled training datasets are highly class-imbalanced, where deep neural networks suffer from generalizing to a balanced testing criterion. In this paper, we explore a novel yet simple way to alleviate this issue by augmenting less-frequent classes via translating samples from more-frequent classes. Our experimental results on a variety of class-imbalanced datasets show that the proposed method improves the generalization on minority classes significantly compared to other existing re-sampling or re-weighting methods.
  arXiv  Detail & Related papers  (2020-04-01T13:21:17Z)

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.