Self-supervised regularization improves semi-supervised learning performance without requiring unlabeled data.
problem Improving semi-supervised learning performance with limited labeled data.
method Introducing self-supervised regularization as a new approach to combine unlabeled data features.
result Self-supervised regularization significantly improves semi-supervised performance on image classification benchmarks.
Unified framework for supervised classification with diverse training data.
problem Handling different types of training data for supervised classification.
method Generalized robust risk minimization (GRRM) with probabilistic transformations.
result GRRM can handle various training data types and new supervision schemes.
Optimal and safe semi-supervised learning estimator for high-dimensional data.
problem Improving regression parameter estimation with unlabeled data in high-dimensional settings.
method Established minimax lower bound, proposed optimal and safe semi-supervised estimators.
result Optimal semi-supervised estimator achieves the minimax lower bound.
Paper proposes an algorithm to recover full supervision from weakly labeled data.
problem Machine learning requires expensive data annotation, motivating the use of weak supervision.
method The paper introduces a disambiguation principle and an empirical disambiguation algorithm for partial labelling.
result The algorithm achieves exponential convergence rates under learnability assumptions.
Paper tackles ride-sharing user experience enhancement with weakly supervised learning.
problem Compound weakly supervised learning problem in ride-sharing comment data.
method CWSL method with instance reweighting, robust criteria, and alternating optimization.
result Effectiveness validated on Didi ride-sharing comment data.
Paper develops methods for semi-supervised Fréchet regression.
problem High costs of obtaining non-Euclidean labels.
method Proposes semi-supervised NW Fréchet regression and semi-supervised kNN Fréchet regression.
result Demonstrates superior performance over supervised methods.
Study shows adding unlabelled data improves semi-supervised image segmentation accuracy.
problem Improving semi-supervised image segmentation accuracy with limited labelled data.
method Investigated the impact of varying labelled and unlabelled data quantities in a semi-supervised segmentation algorithm.
result Significantly higher segmentation accuracy achieved with semi-supervised approach compared to supervised learning.
New framework combines semi-supervised data programming with subset selection for improved text classification.
problem Sub-optimal performance in data programming with noisy labelling functions.
method Introduces a framework \model for semi-supervised data programming that uses joint models and subset selection.
result Significantly outperforms state-of-the-art on seven datasets.
A new method for weakly supervised learning that improves model accuracy.
problem Training machine learning models with precise labels is expensive; weak supervision provides a low-cost alternative.
method Data consistent weak supervision algorithm that searches over classifiers to find plausible labelings, considering features of the training data and estimating labels for low/no coverage data.
result Empirically, the method significantly outperforms state-of-the-art weak supervision methods on text and image classification tasks.
Survey explores theoretical limits and gains of semi-supervised learning.
problem Improving supervised methods using unlabeled data.
method Analysis of different semi-supervised learning methods and their assumptions.
result Understanding the limits and potential of semi-supervised learning.
Dugong models multi-resolution weak supervision for sequential data.
problem Estimating unknown accuracies and correlations of weak supervision sources for sequential data.
method Dugong, a framework that models multi-resolution weak supervision sources with complex correlations, using parameter sharing to improve sample complexity.
result Dugong outperforms traditional supervision by 36.8 F1 points on clinician-validated labels for biomedical video repositories.
The scarcity of data annotated at the desired level of granularity is a recurring issue in many applications. Significant amounts of effort have been devoted to developing weakly supervised methods tailored to each individual setting, which are often carefully designed to take advantage of the particular properties of …
ModSSC unifies semi-supervised classification for various data types.
problem Fragmented support for semi-supervised classification across different methods, settings, and data types.
method ModSSC is a modular Python framework that supports reproducible and controlled experimentation for semi-supervised classification on heterogeneous data.
result ModSSC enables systematic comparison of semi-supervised learning across various datasets and model backbones.
Paper calculates the benefit of unlabeled data in semi-supervised learning for Gaussian mixtures.
problem Improving performance in semi-supervised learning with unlabeled data.
method Analytical computation of Bayes risk gap between supervised and semi-supervised approaches for Gaussian mixture models.
result Quantifies the accuracy increase due to unlabeled data in a Bayesian setting.
Generative models enhance weak supervision for better image classification.
problem Lack of labeled data in supervised learning.
method Fusion of generative adversarial networks and weak supervision.
result Model improves multiclass image classification performance.
Subset selection improves weak supervision performance.
problem Optimizing the use of weakly-labeled data.
method Combining pretrained data representations with the cut statistic for subset selection.
result Subset selection improves weak supervision performance by up to 19%.
New research shows unlabeled data is equally valuable as labeled data in certain semi-supervised learning scenarios.
problem Improving learning performance with limited labeled data.
method Statistical models with continuous parameters, showing equal utility of unlabeled data under specific conditions.
result The learning rate of semi-supervised learning scales similarly to supervised learning when unlabeled data is abundant.
We introduce a new model for building conditional generative models in a semi-supervised setting to conditionally generate data given attributes by adapting the GAN framework. The proposed semi-supervised GAN (SS-GAN) model uses a pair of stacked discriminators to learn the marginal distribution of the data, and the co…
Inference of gene regulatory network from expression data is a challenging task. Many methods have been developed to this purpose but a comprehensive evaluation that covers unsupervised, semi-supervised and supervised methods, and provides guidelines for their practical application, is lacking. We performed an extensiv…
New method improves OSSL by learning from all unlabeled data.
problem Handling open-set semi-supervised learning with unknown classes.
method Self-supervision and energy-based score for all unlabeled data.
result State-of-the-art results on benchmark problems.
Paper resolves the debate on process vs. outcome supervision in reinforcement learning.
problem Distinguishing between process and outcome supervision in reinforcement learning.
method Developed a technical tool (Change of Trajectory Measure Lemma) to show equivalence between outcome and process supervision under standard data coverage assumptions.
result Reinforcement learning through outcome supervision is statistically equivalent to process supervision, up to polynomial factors in horizon.
Survey on self-supervised pre-training for neural networks using unlabeled data.
problem Improving model performance using unlabeled data.
method Pre-training on unlabeled data followed by task-specific adaptation.
result Enhanced model performance through self-supervised pre-training.
RoPAWS improves semi-supervised learning on uncurated data.
problem Efficiency of semi-supervised learning with real-world unlabeled data.
method Reinterprets PAWS as a generative classifier and calibrates predictions using densities of labeled and unlabeled data.
result Significant improvement in performance for uncurated data.
The paper argues all machine learning is supervised, challenging the term 'unsupervised learning'.
problem The categorization of machine learning as supervised vs unsupervised is misleading.
method Analyzes clustering and dimensionality reduction algorithms to argue they are internally supervised.
result All machine learning is internally supervised, challenging the term 'unsupervised learning'.
In this paper, we study statistical properties of semi-supervised learning, which is considered as an important problem in the community of machine learning. In the standard supervised learning, only the labeled data is observed. The classification and regression problems are formalized as the supervised learning. In s…
Ladder Networks improve semi-supervised hyperspectral image classification.
problem Semi-supervised hyperspectral image classification with limited labeled data.
method Jointly optimizing a supervised and unsupervised cost in a Ladder Network.
result Convolutional Ladder Network achieves state-of-the-art performance with minimal labeled data.
New insights into how data transformations affect self-supervised clustering.
problem Impact of data transformations on self-supervised clustering convergence.
method Theoretical and empirical analysis of various data transformations.
result Certain transformations help in faster convergence of self-supervised clustering.
Paper proposes a framework to improve weakly supervised learning performance.
problem Weakly supervised data often lead to poor performance due to unreliable labels.
method Guides label quality optimization using a small validation set.
result Framework achieves impressive performance gains with minimal validation data.
Improves label propagation for weakly supervised learning.
problem Reducing the need for labeled data in machine learning.
method Label Propagation with Weak Supervision (LPA) analysis.
result Demonstrated improvements over existing methods on weakly supervised classification tasks.
DoubleMatch combines pseudo-labeling with self-supervision for SSL.
problem Lack of effective use of unlabeled data in SSL.
method Combines pseudo-labeling with self-supervised loss.
result Achieves state-of-the-art accuracies on multiple datasets.
In several domains obtaining class annotations is expensive while at the same time unlabelled data are abundant. While most semi-supervised approaches enforce restrictive assumptions on the data distribution, recent work has managed to learn semi-supervised models in a non-restrictive regime. However, so far such appro…
Large amounts of labeled data are typically required to train deep learning models. For many real-world problems, however, acquiring additional data can be expensive or even impossible. We present semi-supervised deep kernel learning (SSDKL), a semi-supervised regression model based on minimizing predictive variance in…
Improved audio classification with limited labels using multitask and self-supervised learning.
problem Limited labeled data for audio classification.
method Multitask learning and self-supervised learning on unlabeled data.
result Significant improvement in performance (up to 6%) through multitask and self-supervised learning.
Better use of unlabelled data improves Bayesian active learning models.
problem Neglecting unlabelled data harms predictive performance and data acquisition decisions.
method A simple framework for semi-supervised Bayesian active learning.
result The proposed framework produces better models than conventional approaches.
Labeled data used for training activity recognition classifiers are usually limited in terms of size and diversity. Thus, the learned model may not generalize well when used in real-world use cases. Semi-supervised learning augments labeled examples with unlabeled examples, often resulting in improved performance. Howe…
A new SS-SOM method combines unlabeled and labeled data for clustering and classification.
problem Combining unlabeled and labeled data for clustering and classification in high-dimensional datasets.
method Semi-Supervised Self-Organizing Map (SS-SOM) that dynamically switches between supervised and unsupervised learning.
result SS-SOM outperforms other methods in low-labeled sample conditions and achieves good results with all labeled samples.
Paper tackles RUL prediction with scarce data using indirect supervision.
problem Predicting RUL with indirect supervision and scarce time series data.
method Unified framework called parameterized static regression, handling data scarcity without interpolation.
result Competitive performance in prediction accuracy with simulated data scarcity.
This paper provides an overview of deep semi-supervised learning methods.
problem Reducing the need for large annotated datasets in deep learning.
method Summarizes dominant semi-supervised approaches in deep learning.
result Provides a comprehensive overview of deep semi-supervised learning.
New attacks prevent both supervised and contrastive learning from private data.
problem Preventing unauthorized use of private data and commercial datasets.
method Contrastive-like data augmentations in supervised error minimization or maximization frameworks.
result Achieve state-of-the-art worst-case unlearnability across SL and CL algorithms.
Self-supervised learning improves representation from EEG signals without labels.
problem Limited supervised data for EEG signal analysis.
method Predicting temporal context from unlabeled EEG time series.
result Self-supervised approach outperforms supervised methods in low data regimes.
Novel semi-supervised method for X-ray classification with minimal labels.
problem Classifying X-ray data with limited labeled data.
method Graph-based semi-supervised learning with carefully selected class priors.
result Competitive results on ChestX-ray14 data set with reduced need for annotated data.
Improved voice conversion with semi-supervised learning.
problem Voice conversion with limited parallel data.
method Amortized variational inference with parallel and non-parallel utterances.
result Semi-supervised training improves voice conversion performance.
SSNAS finds neural architectures without labeled data.
problem Limited labeled data for NAS.
method Self-supervised learning for NAS.
result Comparable results to supervised NAS with labeled data.
Study shows semi-supervised learning improves human activity recognition with minimal user input.
problem Improving human activity recognition models using incremental learning.
method Three approaches: non-supervised, semi-supervised, and supervised learning were compared.
result Semi-supervised learning achieves similar accuracy to supervised learning with minimal user input.
Traditionally, there are three species of classification: unsupervised, supervised, and semi-supervised. Supervised and semi-supervised classification differ by whether or not weight is given to unlabelled observations in the classification procedure. In unsupervised classification, or clustering, all observations are …
Improved self-supervised learning for document images.
problem Performance of self-supervised pre-training on document images is poor.
method Proposed context-aware alternatives and a novel multi-modal method.
result Novel method outperforms other self-supervised methods on document image classification.
Paper proposes a novel GCN-based SSL algorithm to enhance node representations using contrastive and generative losses.
problem Shortage of supervision in graph-based semi-supervised learning.
method Combines contrastive and generative graph convolutional networks to enrich supervision signals.
result Improves node representations and classification results on various real-world datasets.
This work tackles continual learning with semi-supervised data, showing that even with minimal labeled data, performance can match full-supervised methods.
problem Training deep networks on a stream of tasks without forgetting, especially when labeled data is scarce.
method Designing a novel CSSL method that leverages metric learning and consistency regularization to learn from both labeled and unlabeled data.
result Our method outperforms state-of-the-art methods trained with full supervision, achieving comparable performance with only 25% labeled data.