SeBA: Semi-supervised few-shot learning via Separated-at-Birth Alignment for tabular data

• URL: http://arxiv.org/abs/2605.08519v1
• Date: Fri, 08 May 2026 22:03:31 GMT
• Title: SeBA: Semi-supervised few-shot learning via Separated-at-Birth Alignment for tabular data
• Abstract summary: We propose Separated-at-Birth Alignment (SeBA) for learning from scarce labeled data with a larger pool of unlabeled samples.<n>SeBA aims to separate the data into two independent, but complementary views and align the representations of one view to mirror the nearest-neighbor correspondence of the data in the second view.<n>An experimental study conducted in various benchmark datasets demonstrates that SeBA achieves the state-of-the-art performance in the majority of cases.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning from scarce labeled data with a larger pool of unlabeled samples, known as semi-supervised few-shot learning (SS-FSL), remains critical for applications involving tabular data in domains like medicine, finance, and science. The existing SS-FSL methods often rely on self-supervised learning (SSL) frameworks developed for vision or language, which assume the availability of a natural form of data augmentations. For tabular data, defining meaningful augmentations is non-trivial and can easily distort semantics, limiting the effectiveness of conventional SSL. In this work, we rethink SSL for tabular data and propose Separated-at-Birth Alignment (SeBA), a joint-embedding framework for SS-FSL that eliminates the dependence on augmentations. Our core idea is to separate the data into two independent, but complementary views and align the representations of one view to mirror the nearest-neighbor correspondence of the data in the second view. Our experimental evaluation supported by a theoretical analysis justifies that SeBA generates an output space, which improves the feature-label relationship. An experimental study conducted in various benchmark datasets demonstrates that SeBA achieves the state-of-the-art performance in the majority of cases, opening a new avenue for SS-FSL paradigm in the domain of tabular data.

Related papers

• Revisiting semi-supervised learning in the era of foundation models [35.44676829010657]
  Semi-supervised learning (SSL) leverages abundant unlabeled data alongside limited labeled data to enhance learning.<n>We develop new SSL benchmark datasets where frozen vision foundation models (VFMs) underperform and systematically evaluate representative SSL methods.<n>We make a surprising observation: parameter-efficient fine-tuning (PEFT) using only labeled data often matches SSL performance, even without leveraging unlabeled data.<n>To overcome the notorious issue of noisy pseudo-labels, we propose ensembling multiple PEFT approaches and VFM backbones to produce more robust pseudo-labels.
  arXiv  Detail & Related papers  (2025-03-12T18:01:10Z)
• On Pretraining Data Diversity for Self-Supervised Learning [57.91495006862553]
  We explore the impact of training with more diverse datasets on the performance of self-supervised learning (SSL) under a fixed computational budget. Our findings consistently demonstrate that increasing pretraining data diversity enhances SSL performance, albeit only when the distribution distance to the downstream data is minimal.
  arXiv  Detail & Related papers  (2024-03-20T17:59:58Z)
• A Survey on Self-Supervised Learning for Non-Sequential Tabular Data [15.796140543132196]
  Self-supervised learning (SSL) has been incorporated into many state-of-the-art models in various domains. This survey aims to systematically review and summarize the recent progress and challenges of SSL for non-sequential data (SSL4NS-TD) We first present a formal definition of NS-TD and clarify its correlation to related studies. Then, these approaches are categorized into three groups - predictive learning, contrastive learning, and hybrid learning, with their motivations and strengths of representative methods in each direction.
  arXiv  Detail & Related papers  (2024-02-02T08:17:41Z)
• Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects [84.6945070729684]
  Self-supervised learning (SSL) has recently achieved impressive performance on various time series tasks. This article reviews current state-of-the-art SSL methods for time series data.
  arXiv  Detail & Related papers  (2023-06-16T18:23:10Z)
• Semi-supervised Learning with Deterministic Labeling and Large Margin Projection [25.398314796157933]
  The centrality and diversity of the labeled data are very influential to the performance of semi-supervised learning (SSL) This study is to learn a kernelized large margin metric for a small amount of most stable and most divergent data that are recognized based on the OLF structure. Attribute to this novel design, the accuracy and performance stableness of the SSL model based on OLF is significantly improved compared with its baseline methods.
  arXiv  Detail & Related papers  (2022-08-17T04:09:35Z)
• MET: Masked Encoding for Tabular Data [29.24328720103911]
  Masked SSL for Tabular Data (MET) is based on the popular MAE approach for vision-SSL. MET achieves a new state of the art (SOTA) on five diverse datasets. We shed more light on the working of MET via experiments on carefully designed simple datasets.
  arXiv  Detail & Related papers  (2022-06-17T05:51:10Z)
• Collaborative Intelligence Orchestration: Inconsistency-Based Fusion of Semi-Supervised Learning and Active Learning [60.26659373318915]
  Active learning (AL) and semi-supervised learning (SSL) are two effective, but often isolated, means to alleviate the data-hungry problem. We propose an innovative Inconsistency-based virtual aDvErial algorithm to further investigate SSL-AL's potential superiority. Two real-world case studies visualize the practical industrial value of applying and deploying the proposed data sampling algorithm.
  arXiv  Detail & Related papers  (2022-06-07T13:28:43Z)
• 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)
• Self-supervised Learning is More Robust to Dataset Imbalance [65.84339596595383]
  We investigate self-supervised learning under dataset imbalance. Off-the-shelf self-supervised representations are already more robust to class imbalance than supervised representations. We devise a re-weighted regularization technique that consistently improves the SSL representation quality on imbalanced datasets.
  arXiv  Detail & Related papers  (2021-10-11T06:29:56Z)
• Trash to Treasure: Harvesting OOD Data with Cross-Modal Matching for Open-Set Semi-Supervised Learning [101.28281124670647]
  Open-set semi-supervised learning (open-set SSL) investigates a challenging but practical scenario where out-of-distribution (OOD) samples are contained in the unlabeled data. We propose a novel training mechanism that could effectively exploit the presence of OOD data for enhanced feature learning. Our approach substantially lifts the performance on open-set SSL and outperforms the state-of-the-art by a large margin.
  arXiv  Detail & Related papers  (2021-08-12T09:14:44Z)

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.