Constrained Few-Shot Learning: Human-Like Low Sample Complexity Learning and Non-Episodic Text Classification

• URL: http://arxiv.org/abs/2208.08089v2
• Date: Thu, 7 Dec 2023 08:22:34 GMT
• Title: Constrained Few-Shot Learning: Human-Like Low Sample Complexity Learning and Non-Episodic Text Classification
• Authors: Jaron Mar and Jiamou Liu
• Abstract summary: Few-shot learning is an emergent paradigm of learning that attempts to learn to reason with low sample complexity. We propose a method for CFSL leveraging Cat2Vec using a novel categorical contrastive loss inspired by cognitive theories.
• Score: 11.35732215154172
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Few-shot learning (FSL) is an emergent paradigm of learning that attempts to learn to reason with low sample complexity to mimic the way humans learn, generalise and extrapolate from only a few seen examples. While FSL attempts to mimic these human characteristics, fundamentally, the task of FSL as conventionally formulated using meta-learning with episodic-based training does not in actuality align with how humans acquire and reason with knowledge. FSL with episodic training, while only requires $K$ instances of each test class, still requires a large number of labelled training instances from disjoint classes. In this paper, we introduce the novel task of constrained few-shot learning (CFSL), a special case of FSL where $M$, the number of instances of each training class is constrained such that $M \leq K$ thus applying a similar restriction during FSL training and test. We propose a method for CFSL leveraging Cat2Vec using a novel categorical contrastive loss inspired by cognitive theories such as fuzzy trace theory and prototype theory.

Related papers

• Instance-based Max-margin for Practical Few-shot Recognition [32.26577845735846]
  IbM2 is a novel instance-based max-margin method for few-shot learning. This paper shows that IbM2 almost always leads to improvements compared to its respective baseline methods.
  arXiv  Detail & Related papers  (2023-05-27T04:55:13Z)
• OpenLDN: Learning to Discover Novel Classes for Open-World Semi-Supervised Learning [110.40285771431687]
  Semi-supervised learning (SSL) is one of the dominant approaches to address the annotation bottleneck of supervised learning. Recent SSL methods can effectively leverage a large repository of unlabeled data to improve performance while relying on a small set of labeled data. This work introduces OpenLDN that utilizes a pairwise similarity loss to discover novel classes.
  arXiv  Detail & Related papers  (2022-07-05T18:51:05Z)
• Semantics-driven Attentive Few-shot Learning over Clean and Noisy Samples [0.0]
  We aim to train meta-learner models that can leverage prior semantic knowledge about novel classes to guide the classifier synthesis process. In particular, we propose semantically-conditioned feature attention and sample attention mechanisms that estimate the importance of representation dimensions and training instances.
  arXiv  Detail & Related papers  (2022-01-09T16:16:23Z)
• A Strong Baseline for Semi-Supervised Incremental Few-Shot Learning [54.617688468341704]
  Few-shot learning aims to learn models that generalize to novel classes with limited training samples. We propose a novel paradigm containing two parts: (1) a well-designed meta-training algorithm for mitigating ambiguity between base and novel classes caused by unreliable pseudo labels and (2) a model adaptation mechanism to learn discriminative features for novel classes while preserving base knowledge using few labeled and all the unlabeled data.
  arXiv  Detail & Related papers  (2021-10-21T13:25:52Z)
• Towards Zero-Shot Learning with Fewer Seen Class Examples [41.751885300474925]
  We present a meta-learning based generative model for zero-shot learning (ZSL) This setup contrasts with the conventional ZSL approaches, where training typically assumes the availability of a sufficiently large number of training examples from each of the seen classes. We conduct extensive experiments and ablation studies on four benchmark datasets of ZSL and observe that the proposed model outperforms state-of-the-art approaches by a significant margin when the number of examples per seen class is very small.
  arXiv  Detail & Related papers  (2020-11-14T11:58:35Z)
• Interventional Few-Shot Learning [88.31112565383457]
  We propose a novel Few-Shot Learning paradigm: Interventional Few-Shot Learning. Code is released at https://github.com/yue-zhongqi/ifsl.
  arXiv  Detail & Related papers  (2020-09-28T01:16:54Z)
• Boosting Few-Shot Learning With Adaptive Margin Loss [109.03665126222619]
  This paper proposes an adaptive margin principle to improve the generalization ability of metric-based meta-learning approaches for few-shot learning problems. Extensive experiments demonstrate that the proposed method can boost the performance of current metric-based meta-learning approaches.
  arXiv  Detail & Related papers  (2020-05-28T07:58:41Z)
• TAFSSL: Task-Adaptive Feature Sub-Space Learning for few-shot classification [50.358839666165764]
  We show that the Task-Adaptive Feature Sub-Space Learning (TAFSSL) can significantly boost the performance in Few-Shot Learning scenarios. Specifically, we show that on the challenging miniImageNet and tieredImageNet benchmarks, TAFSSL can improve the current state-of-the-art in both transductive and semi-supervised FSL settings by more than $5%$.
  arXiv  Detail & Related papers  (2020-03-14T16:59:17Z)
• Few-Shot Learning as Domain Adaptation: Algorithm and Analysis [120.75020271706978]
  Few-shot learning uses prior knowledge learned from the seen classes to recognize the unseen classes. This class-difference-caused distribution shift can be considered as a special case of domain shift. We propose a prototypical domain adaptation network with attention (DAPNA) to explicitly tackle such a domain shift problem in a meta-learning framework.
  arXiv  Detail & Related papers  (2020-02-06T01:04:53Z)

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.