Curriculum Labeling improves semi-supervised learning with pseudo-labeling, achieving high accuracy with minimal labeled data.
problem Improving semi-supervised learning with limited labeled data.
method Applying curriculum learning principles and restarting model parameters before each self-training cycle.
result 94.91% accuracy on CIFAR-10 with only 4,000 labeled samples.
Doubly robust self-training improves semi-supervised learning by balancing labeled and pseudo-labeled data.
problem Improving semi-supervised learning performance with limited labeled data.
method Introduces doubly robust self-training, a method that combines labeled and pseudo-labeled data to balance between labeled-only and pseudo-labeled-only training.
result Demonstrates superior performance of doubly robust self-training on ImageNet and nuScenes datasets.
Contrastive regularization improves semi-supervised learning by better propagating confident pseudo-labels.
problem Consistency regularization's limitation in high performance and efficiency.
method Proposes contrastive regularization to update model features, pushing confident labels into unlabeled samples.
result Improves semi-supervised learning tasks with fewer training iterations and robust performance.
A neural network method estimates densities from characteristic functions.
problem Estimating fixed-horizon probability densities from empirical characteristic functions.
method Data-driven Fourier-mixture neural-network method trained in Fourier space.
result Competitive performance and clear gains on heavy-tailed targets.
MTL method uses unlabeled data with pseudo labels to improve classification with disjoint datasets.
problem Improving classification performance with disjoint labeled datasets using unlabeled data.
method Proposes MTL-SA method to select and augment unlabeled data with confident pseudo labels and close distribution to labeled data.
result Extensive experiments show the effectiveness of MTL-SA method in improving classification performance.
Method transfers knowledge without label overlap, source data, or target architecture consistency.
problem Difficulties in transfer learning due to label mismatch, restricted source data, and specialized target architectures.
method Uses deep generative models in two stages: pseudo pre-training and pseudo semi-supervised learning.
result Outperforms scratch training and knowledge distillation methods.
The paper proposes using a discriminator for both domain adaptation and pseudo labeling confidence.
problem Improving generalization of classifiers trained on labeled source data to unlabeled target data.
method Multi-purposing the discriminator to learn domain-invariant feature representations and generate pseudo labels based on confidence.
result The approach enhances classifier performance by providing confidence measures for pseudo labels.
Selective pseudo-labeling improves unsupervised domain adaptation.
problem Classifying unlabeled target domain samples with labeled source domain samples.
method Structured prediction for selective pseudo-labeling.
result Selective pseudo-labeling outperforms state-of-the-art methods.
Mutual teaching improves graph models with less labeled data.
problem Training graph models with limited labeled data.
method Dual model training with mutual teaching strategy.
result Significant performance improvement with less labeled data.
Self-training improves model accuracy by refining pseudo-labels.
problem Improving semi-supervised learning with self-training.
method Theoretical insights into self-training algorithm with a focus on linear classifiers.
result Self-training iterations can improve model accuracy even if stuck in sub-optimal fixed points.
A new confidence measure improves self-training in biased data.
problem Improving self-training in biased data.
method Proposes a new confidence measure, T-similarity, based on ensemble diversity of linear classifiers.
result Empirically shows the benefit of T-similarity for pseudo-labeling policies on various datasets.
Proposes a new semi-supervised learning method to reduce distribution mismatch.
problem Empirical distribution mismatch between labeled and unlabeled data in semi-supervised learning.
method Adversarial training and interpolation strategy to align labeled and unlabeled data distributions.
result Demonstrates improved performance on benchmark datasets SVHN and CIFAR10.
PNDMs accelerate DDPMs by treating them as differential equations on manifolds.
problem Accelerate DDPMs while maintaining sample quality.
method Propose pseudo numerical methods (PNDMs) to solve differential equations on manifolds.
result PNDMs generate higher quality images with only 50 steps compared to 1000-step DDIMs (20x speedup).
Self-training algorithm improves classifier performance with labeled and unlabeled data.
problem Improving classifier performance with limited labeled data.
method Iterative learning of halfspaces, exploration and pruning phases.
result Misclassification error is bounded and never degrades compared to initial labeled set.
Proposes a novel model-agnostic training procedure for anomaly detection incorporating known anomalies.
problem Challenges of anomaly detection, especially when only a few anomalous samples are available.
method Reformulates one-class classification as a binary classification problem, using pseudo-anomalous samples drawn from a normalizing flow model.
result Demonstrates comparable or superior performance on tasks with variable amounts of known anomalies.
Bayesian Pseudo Label Selection reduces overfitting in semi-supervised learning.
problem Overfitting in pseudo-label selection for semi-supervised learning.
method BPLS, a Bayesian framework that approximates the posterior predictive of pseudo-samples.
result BPLS outperforms traditional PLS methods, especially in high-dimensional data.
Zero-shot KD for object detection without training data.
problem Challenges in using training data for knowledge distillation.
method Synthesizes pseudo-targets and samples using pretrained network.
result Achieves respectable mAP on object detection benchmarks.
RegMixMatch optimizes Mixup for semi-supervised learning by integrating high- and low-confidence samples.
problem Mixup degrades SSL performance by compromising artificial labels purity.
method RegMixMatch integrates high- and low-confidence samples, uses class-aware Mixup, and mitigates confirmation bias.
result RegMixMatch achieves state-of-the-art performance in SSL benchmarks.
New method improves sampling from noisy energy models.
problem Training and sampling challenges in Energy-Based Models.
method Pseudo-Gibbs sampling with moment matching.
result Effective sampling from clean model using noisy DSM-trained model.
Method uses pseudo-samples to improve RCT data in ride-hailing pricing studies.
problem Small and biased RCT data leads to significant bias when generalizing to broader user base.
method Pseudo-sample matching to expand and match RCT data with observational data.
result 0.41% improvement in profit through pseudo-sample matching.
Paper translates train track concepts to cluster algebras for pseudo-Anosov mapping classes.
problem Understanding pseudo-Anosov mapping classes on surfaces.
method Using Goncharov--Shen's potential function, the paper translates train track concepts into cluster algebra language.
result Proves sign stability of general pseudo-Anosov mapping classes.
Study Agol cycles in pseudo-Anosov 3-braids.
problem Understanding conjugacy invariants of pseudo-Anosov maps.
method Investigate train tracks associated with Farey intervals and describe Agol cycles.
result Complete description of Agol cycles in pseudo-Anosov 3-braids.
iTimER learns from reconstruction errors to represent irregularly sampled time series.
problem Learning from irregularly sampled time series with missing data.
method iTimER models reconstruction errors as a proxy for unobserved values, using a mixup strategy and a Wasserstein metric.
result iTimER outperforms state-of-the-art methods in classification, interpolation, and forecasting tasks.
TPA-AD detects axle-box bearing anomalies using pseudo anomalies near normal boundaries.
problem Detecting axle-box bearing anomalies with only normal training data.
method Two-stage approach: pseudo anomalies, contrastive learning, KNN.
result Improves anomaly detection separability and sensitivity to degradation.
Proposes ConstraintMatch for semi-supervised clustering with unconstrained data.
problem Leveraging unconstrained data alongside constraints for clustering models.
method Semi-supervised context with pseudo-constraining and pseudo-labeling mechanisms.
result Demonstrates effectiveness of ConstraintMatch over baselines.
Paper improves short text clustering by integrating semantic relationships into Optimal Transport.
problem Erroneous pseudo-labels caused by neglecting semantic consistency in existing OT methods.
method Designs an instance-level attention mechanism to capture semantic relationships and integrates them into the OT formulation.
result Generates reliable pseudo-labels that improve clustering accuracy.
Sharp sample complexity for multiclass PAC learning with bandit feedback.
problem Multiclass PAC learning with bandit feedback.
method Characterization of optimal sample complexity using a new combinatorial dimension (bandit DS dimension) and a learning algorithm (ListCascade).
result Sharp sample complexity characterization for every concept class up to logarithmic factors.
A new method for domain generalization using unlabeled data.
problem Learning from multiple domains with limited labeled data.
method Combines meta learning and semi-supervised learning with entropy-based pseudo-labeling and discrepancy loss.
result Significantly outperforms state-of-the-art methods on benchmark datasets.
Framework for domain adaptation using pseudo-labels from unlabeled data.
problem Improving prediction accuracy in target domain with covariate shift.
method Kernel GLMs with labeled and pseudo-labeled data, using imputation model for target data.
result Non-asymptotic excess-risk bounds for effective labeled sample size.
Pseudo rehearsal uses non-photo-realistic images to save resources without sacrificing performance.
problem Catastrophic forgetting in neural networks when learning new tasks.
method Synthetically generate non-photo-realistic images to rehearse previous tasks.
result Non-photo-realistic images can be used for rehearsal without sacrificing performance and significantly reduce resource consumption.
New train tracks for complex homeomorphisms found.
problem Existence of irreducible train tracks for pseudo-Anosov homeomorphisms.
method Starting from a veering triangulation, identify and modify branches to bypass obstructions.
result Construction of invariant train tracks with irreducible transition matrix.
The study of pseudo-Anosov maps with minimum expansion factor using train tracks.
problem Finding pseudo-Anosov maps with minimum expansion factor.
method Analysis of standardly embedded train tracks and Thurston symplectic form.
result The expansion factor of pseudo-Anosov maps is bounded by a specific inequality involving the golden ratio.
Sampling from posterior distributions using Markov chain Monte Carlo (MCMC) methods can require an exhaustive number of iterations, particularly when the posterior is multi-modal as the MCMC sampler can become trapped in a local mode for a large number of iterations. In this paper, we introduce the pseudo-extended MCMC…
A new SSL method uses instance-dependent thresholds to improve accuracy.
problem Improving semi-supervised learning by better selecting confident unlabeled instances.
method Proposes instance-dependent thresholds that vary based on the ambiguity and error rates of pseudo-labels for each unlabeled instance.
result Demonstrates that instance-dependent thresholds provide a probabilistic guarantee for correct pseudo-labels.
PostNet predicts uncertainty without OOD data, improving OOD detection and calibration.
problem Accurate uncertainty estimation for safe systems.
method PostNet uses Normalizing Flows to learn individual posterior distributions over predicted probabilities.
result PostNet achieves state-of-the-art results in OOD detection and uncertainty calibration.
This note gives a brief survey of the minimum dilatation problem for pseudo-Anosov mapping classes, and the first explicit train track description of an infinite family of pseudo-Anosov mapping classes with orientable stable foliations and the conjectural minimum dilatation for closed surfaces of even genus g≥2.
AV-CPL uses continuous pseudo-labels for AVSR combining labeled and unlabeled data.
problem Improving AVSR performance with labeled and unlabeled data.
method Semi-supervised method using continuous pseudo-labels generated by the same AVSR model.
result Significant improvements in VSR performance on LRS3 dataset.
The paper improves self-training in semi-supervised learning by selecting more robust pseudo-labeled data.
problem Improving the reliability of pseudo-labeled data selection in self-training for semi-supervised learning.
method Proposes a multi-objective utility function to select pseudo-labeled data that maximizes reliability, considering model selection, accumulation of errors, and covariate shift uncertainties.
result Robustness towards model choice can lead to substantial accuracy gains in self-training.
Bayesian inference in the presence of an intractable likelihood function is computationally challenging. When following a Markov chain Monte Carlo (MCMC) approach to approximate the posterior distribution in this context, one typically either uses MCMC schemes which target the joint posterior of the parameters and some…
A new metric predicts model performance on unseen data.
problem Predicting performance on out-of-distribution data without labels.
method Uses model predictions to pseudo-label data, trains a new model, and measures difference from in-distribution models.
result Empirically outperforms existing methods on image and text classification tasks.
Deep multitask learning boosts performance by sharing learned structure across related tasks. This paper adapts ideas from deep multitask learning to the setting where only a single task is available. The method is formalized as pseudo-task augmentation, in which models are trained with multiple decoders for each task.…
POTA improves short text clustering by generating reliable pseudo-labels.
problem Limited discriminative representations in short texts.
method POTA uses instance-level attention and optimal transport for semantic consistency and cluster structure.
result POTA outperforms state-of-the-art methods in short text clustering.
Paper tackles cross-granularity few-shot learning with meta-embedder.
problem Few-shot learning with coarse labels and fine-grained testing.
method Meta-embedder that optimizes visual and semantic discrimination across coarse and fine classes.
result Meta-embedder achieves effective cross-granularity few-shot classification.
Study Agol cycles on 2-punctured torus and 5-punctured sphere, finding new dilatation formula.
problem Understanding Agol cycles on specific surfaces.
method Computed measured train tracks and Agol cycles for pseudo-Anosov maps.
result Found a new formula for the dilatation of pseudo-Anosov maps.
New method predicts positive samples with missing labels.
problem Missing labels due to response-dependent factors.
method P(U)U-O-Mixture algorithm for joint estimation.
result Non-convex algorithm leads to optimal statistical error.
The training of Generative Adversarial Networks is a difficult task mainly due to the nature of the networks. One such issue is when the generator and discriminator start oscillating, rather than converging to a fixed point. Another case can be when one agent becomes more adept than the other which results in the decre…
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.
FedCVT improves VFL models with limited aligned samples.
problem Limited aligned samples in VFL.
method Cross-view training to estimate missing features and predict pseudo-labels.
result FedCVT significantly outperforms vanilla VFL.