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.
Paper proposes a pipeline for weakly supervised learning in object detection and segmentation.
problem Challenges in weakly supervised learning for object detection and segmentation with only image-level labels.
method Curriculum learning pipeline with object localization, filtering, fusing, and pixel labeling.
result State-of-the-art results in multi-label image classification and weakly supervised object detection.
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.
Self-supervised attention model improves weakly labeled audio event classification.
problem Efficiently classify audio events with minimal labeled data.
method Develops a self-supervised attention model for weakly labeled audio clips.
result Self-supervised attention model performs comparably to strongly supervised model trained with strong labels.
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.
Efficiently predicts paths in hierarchical text classification using unlabeled data.
problem Costly labeling of documents in hierarchical text classification.
method Path cost-sensitive learning algorithm using generative model and path constraints.
result Significantly reduces computational cost and improves efficiency.
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 …
New method improves machine learning in physics.
problem Improving machine learning performance in physics with limited data.
method Weakly supervised classification using class proportions as input.
result Weakly supervised classification matches fully supervised algorithms in quark vs gluon tagging.
Unified approach for multicalibration in weakly supervised learning.
problem Existing multicalibration methods require clean input-label pairs, which are unavailable in weakly supervised learning.
method Developed estimators and post-hoc correction methods for multicalibration under weak supervision.
result Unified framework for estimating and correcting multicalibration under weak supervision with finite-sample guarantees.
Proposes a constrained labeling method for weakly supervised learning.
problem Combining weak supervision signals while navigating misleading correlations.
method Randomized constrained labeling within a defined space.
result Randomized constrained labeling converges after few iterations and outperforms other methods.
Paper tackles weakly supervised learning from similarity-confidence data.
problem Learning binary classifier from unlabeled data pairs with confidence of similarity.
method Proposes an unbiased estimator of classification risk from Sconf data and risk correction scheme.
result Demonstrates effectiveness of proposed methods through experiments.
The paper explores how supervision level affects both statistical accuracy and computational efficiency in weakly supervised binary classification.
problem The impact of label flip probability on statistical and computational efficiency in weakly supervised binary classification.
method Information-theoretic and computational boundaries were established to characterize the relationship between supervision level and performance.
result The gap between statistical and computational boundaries narrows as the supervision level increases, indicating improved computational efficiency with more supervision.
Novel ramp loss method improves weakly supervised machine translation and parsing.
problem Training neural models without gold labels in weak supervision scenarios.
method Adapted ramp loss objectives to promote positive outputs and discourage negative ones.
result Bipolar ramp loss objectives outperform other methods on weakly supervised tasks.
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.
PPN learns from weakly-labeled data to improve few-shot learning.
problem Few-shot learning with limited labeled data.
method Prototype Propagation Network (PPN) trained on few-shot tasks with coarse-label data.
result PPN significantly outperforms other methods on benchmarks.
A method for semi-supervised sound event detection using teacher-student learning.
problem Weakly-labeled data in sound event detection.
method Guided Learning with a teacher model for audio tagging and a student model for boundary detection.
result The method improves boundary detection performance using unlabeled data.
Paper develops proper, lower-bounded losses for weakly supervised classification.
problem Weakly supervised classification with corrupted labels.
method Representation theorem for proper losses, derived condition for lower-boundedness, generalized logit squeezing.
result Proper and lower-bounded losses for weak-label learning.
Method trains classifiers without labels using adversarial constraints.
problem Training classifiers without labeled data.
method Adversarial label learning method that trains classifiers to perform well against an adversary choosing labels.
result Method outperforms other weakly supervised learning approaches on real datasets.
ParsNet tackles weakly supervised data streams with a self-evolving deep neural network.
problem Weakly supervised data streams hinder existing data stream algorithms.
method ParsNet uses a self-labelling strategy with hedge (SLASH) and a closed-loop configuration of generative and discriminative training processes.
result ParsNet outperforms other methods in high-dimensional data streams and infinite delay simulations.
Method trains deep neural networks on weakly labeled audio data efficiently.
problem Limited training data and lack of temporal labels for audio event detection.
method Multi-instance learning with a new loss function for stacked CNN-RNN.
result Improved performance on low-resource audio datasets.
TrustNet robustly learns noise patterns from trusted data to improve weakly-supervised classification.
problem Robustness to label noise in weakly-supervised learning.
method TrustNet learns noise patterns from trusted data, then trains a robust classifier using these patterns.
result TrustNet outperforms state-of-the-art methods in robustness to various noise patterns.
Consider a classification problem where we do not have access to labels for individual training examples, but only have average labels over subpopulations. We give practical examples of this setup and show how such a classification task can usefully be analyzed as a weakly supervised clustering problem. We propose thre…
Paper improves sound event detection using semi-supervised learning.
problem Weakly labeled sound event detection in polyphonic audio clips.
method Combines tri-training and adversarial learning for semi-supervised learning.
result Significant performance improvement over baseline model.
Paper proposes a method to generate instance labels from weakly supervised data.
problem Weakly supervised instance labeling in medical image analysis.
method Uses multiple instance learning (MIL) and knowledge distillation to generate instance-level predictions.
result Significantly outperforms state-of-the-art MIL methods in instance-level prediction.
Model counts event occurrences to detect and locate events in data.
problem Training deep neural networks with precise event locations.
method Weakly-supervised learning using occurrence counts.
result Comparable performance to fully-supervised methods with weaker training data.
Proposes a method to create predictive sets from partially labeled data.
problem Efficiently using weakly supervised data for structured prediction tasks.
method Introduces probe functions and a false discovery proportion-type loss.
result Validates the effectiveness of the proposed predictive set construction.
The paper provides a framework for weakly supervised disentanglement guarantees.
problem Learning disentangled representations in real-world data.
method Theoretical framework for analyzing disentanglement guarantees with weak supervision.
result Empirical verification of weak supervision methods' predictive power and usefulness.
Paper tackles domain invariant sentiment classification using weak supervision.
problem Learning a sentiment classification model that adapts to any target domain.
method Two-stage training procedure with weakly supervised datasets.
result Transfer learning with weak supervision achieves performance close to supervised training.
New framework for weakly supervised learning from label proportions.
problem Lack of consistent learning procedure and theoretical training criterion for LLP.
method Pose LLP as mutual contamination models (MCMs) and establish unbiased losses and generalization error bounds.
result Established novel technical results for MCMs and proposed a new experimental setting.
Paper uses weakly-supervised clustering to automatically create network protocol abstractions.
problem Manual definition of abstraction by domain experts is time-consuming.
method Weakly supervised clustering algorithm for automatic abstraction.
result The method successfully matches the reference abstraction with minimal labeled examples.
Method learns feature maps from deep CNN layers for weakly supervised chest pathology localization.
problem Localization of chest pathologies in X-ray images is challenging due to varying sizes and appearances.
method Class-aware deep multiscale feature learning using intermediate feature maps from CNN layers.
result Improves localization performance of small pathologies like nodules and masses.
Improved GEC with weakly supervised data and iterative decoding.
problem Grammatical error correction using limited labeled data.
method Transformer model trained on weakly supervised bitext, iterative decoding.
result Iterative decoding improves GEC performance on CoNLL'14 benchmark.
Paper tackles drowsy driving by learning from weakly labeled car acceleration data.
problem Lack of labeled data for estimating driver drowsiness.
method Weakly supervised learning, scalable stochastic optimization.
result Algorithm learns from weakly labeled data, outperforming baseline methods.
Weak supervision challenges black-box models, suggesting fusion of modeling cultures.
problem Challenges of strong supervision in achieving accurate predictions.
method Integrating data modeling into algorithmic modeling for weak supervision.
result Integration of data modeling culture improves model stability and accuracy.
New learning rules achieve optimal sample complexity for weakly supervised classification.
problem Learning from label proportions in weakly supervised settings.
method Debiased proportional square loss and EasyLLP learning rule.
result Achieves optimal sample complexity in both realizable and agnostic settings.
Proposes a differentiable hypergeometric distribution for learning group importance.
problem Learning the sizes of subsets in applications like clustering and weakly-supervised learning.
method Introduces a reparameterizable hypergeometric distribution to model group sizes and learn their relative importance.
result Outperforms previous methods in weakly-supervised learning and clustering.
Paper proposes unsupervised knowledge graph alignment with adversarial learning.
problem Aligning knowledge graphs from different sources or languages without large amounts of aligned triplets.
method Adversarial learning framework to align entity and relation embeddings, with mutual information regularization.
result Framework effectively aligns knowledge graphs in unsupervised and weakly-supervised settings.
Weakly supervised learning for histopathology disease localization.
problem Locating abnormal cells or single cells in histopathology slides.
method Pre-trained deep convolutional networks, feature embedding, top instances and negative evidence.
result Comparable performance to strong annotations on lymph node metastases detection challenge.
This paper introduces a general multi-class approach to weakly supervised classification. Inferring the labels and learning the parameters of the model is usually done jointly through a block-coordinate descent algorithm such as expectation-maximization (EM), which may lead to local minima. To avoid this problem, we pr…
Study shows reverberant phase is not essential for weakly-supervised dereverberation.
problem Evaluating the role of reverberant phase in weakly-supervised dereverberation.
method Statistical Wave Field Theory and recent weak supervision framework.
result Wet phase carries limited useful information and is not essential for weakly supervised dereverberation.
The abstract discusses extending learning objectives to measure theory for better generalization.
problem Improving out-of-distribution generalization and weakly-supervised learning.
method Extending variational learning objectives to measures.
result New objectives on measures may lead to practical algorithms.
Improves data labeling efficiency in machine learning.
problem Efficiency in data labeling for machine learning models.
method Weakly supervised learning, active labeling, stochastic gradient descent.
result Derives a new algorithm for active labeling that scales better with input dimension.
Proposes a method to train deep neural networks with limited labeled data.
problem Lack of labeled data in neural text classification.
method Two modules: pseudo-document generator and self-training module.
result Significantly outperforms baseline methods without excessive labeled data.
A new model learns from multiple types of data without needing complete information.
problem Learning from multiple types of data without complete information.
method Multimodal variational autoencoder (MVAE) with product-of-experts inference network and sub-sampled training.
result Matches state-of-the-art performance with fewer parameters and is robust to incomplete supervision.
Approach for training deep nets with unlabeled patches.
problem Training deep neural networks with detailed expert annotations.
method Cluster-based learning from weakly labeled bags in latent space.
result Improved performance on Camelyon dataset.
Enhances mobile context prediction using weakly supervised learning.
problem Power consumption and limited training data for always-on context prediction.
method Weakly supervised learning framework for multi-modal sensing.
result Personalized context prediction model with improved accuracy.
Unified framework for N-tuples learning improves weakly supervised tasks.
problem Reducing annotation burden in supervised learning.
method Empirical risk minimization framework integrating pointwise unlabeled data.
result Framework improves generalization across various N-tuples learning tasks.
A deep learning framework learns meaningful representations for weakly supervised multiple instance learning.
problem Weakly supervised multiple instance learning with uncertainty in positive instance labels.
method Discriminative model regularized by variational autoencoders to learn latent representations.
result Improved performance on standard benchmark datasets compared to state-of-the-art approaches.