Proposes MGPLL for PL learning with non-random noise.
problem Partial label learning with non-random label noise.
method Bi-directional mapping framework, conditional noise label generation, multi-class predictor, adversarial learning.
result Demonstrates state-of-the-art performance in partial label learning.
PML-GAN tackles noisy multi-label annotations using adversarial learning.
problem Learning multi-label models from noisy, overcomplete annotations.
method PML-GAN uses a disambiguation network and a generative adversarial network to map noisy labels to clean labels and data samples.
result PML-GAN achieves state-of-the-art performance on partial multi-label learning datasets.
A new method for online multi-label stream classification.
problem Challenges in classifying continuous data streams with concept drift and delayed labels.
method Online unsupervised incremental method based on self-organizing maps.
result The method is highly competitive in both stationary and concept drift scenarios.
Improved diffusion map enhances manifold regularization for semi-supervised learning.
problem Limited performance of manifold regularization models in capturing global structure.
method Enhanced diffusion map with improved label propagation function.
result Proposed method improves manifold regularization model's performance.
This paper discusses the effect of hubness in zero-shot learning, when ridge regression is used to find a mapping between the example space to the label space. Contrary to the existing approach, which attempts to find a mapping from the example space to the label space, we show that mapping labels into the example spac…
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.
Earth observation embeddings can convert discrete biome maps into continuous representations that better capture ecological variation.
problem Biome maps impose categorical boundaries that compress continuous variation in biotic communities.
method Fit a linear classifier on Earth observation embeddings to predict biome labels.
result Continuous biome representation outperforms discrete biome labels for predicting species occurrence.
In this paper, we present a reverberation removal approach for speaker verification, utilizing dual-label deep neural networks (DNNs). The networks perform feature mapping between the spectral features of reverberant and clean speech. Long short term memory recurrent neural networks (LSTMs) are trained to map corrupted…
S2cGAN uses fewer labels to train cGANs effectively.
problem Training conditional GANs requires expensive labelled data.
method Semi-supervised training with sparse labels and unsupervised data.
result S2cGAN learns conditional mapping with sparse labels and unconditional distribution with unsupervised data.
Study how noisy labels affect semi-supervised learning.
problem Effect of noisy labels on semi-supervised learning performance.
method Proposed an algorithm derived from a continuous relaxation of the Maximum A Posteriori (MAP) estimator for a Degree Corrected Stochastic Block Model (DC-SBM).
result Our approach achieves promising performance even with very noisy labeled data.
Sourcerer uses deep learning to map land cover from limited labeled data.
problem Producing accurate land cover maps with scarce labeled data.
method Bayesian-inspired, deep learning approach with a novel regularizer.
result Sourcerer outperforms other methods, even with minimal labeled target data.
Gatherings of thousands to millions of people frequently occur for an enormous variety of events, and automated counting of these high-density crowds is useful for safety, management, and measuring significance of an event. In this work, we show that the regularly accepted labeling scheme of crowd density maps for trai…
A new SOM method learns from both labeled and unlabeled data.
problem Improving performance in semi-supervised learning.
method Adaptive Local Thresholds Semi-Supervised Self-Organizing Map (ALTSS-SOM).
result ALTSS-SOM outperforms other methods in classification and clustering.
New model LMRC tackles class incremental learning without needing old classes.
problem Softmax suppression problem in class incremental learning.
method Label Mapping with Response Consolidation (LMRC) and multi-head neural network.
result LMRC achieves better performance than related methods in different scenarios.
Robust image translation model for noisy labels.
problem Learning mappings among multiple domains with noisy labeled data.
method Proposes a novel loss function and techniques to handle noisy labeled data.
result Demonstrates robustness in various settings including synthetic and real-world noise.
We study the natural map eta between a group of binary planar trees whose leaves are labeled by elements of a free abelian group H and a certain group D(H) derived from the free Lie algebra over H. Both of these groups arise in several different topological contexts. The map eta is known to be an isomorphism over Q, bu…
Boosting for label ranking outperforms existing methods.
problem Improving label ranking predictions using boosting techniques.
method Proposed a boosting algorithm tailored for label ranking tasks.
result Significantly outperforms existing label ranking algorithms.
A bidirectional loss function improves label distribution learning and enhancement.
problem Challenges in label distribution learning and label enhancement.
method Bidirectional loss function to address dimensional gap and label enhancement.
result The bidirectional loss function improves the accuracy of label distribution learning and enhancement.
We present label gradient alignment, a novel algorithm for semi-supervised learning which imputes labels for the unlabeled data and trains on the imputed labels. We define a semantically meaningful distance metric on the input space by mapping a point (x, y) to the gradient of the model at (x, y). We then formulate an …
Lower bound proves ridgeless regression performs poorly near interpolation threshold.
problem Proving performance of ridgeless regression near interpolation threshold.
method Distribution-independent lower bound for mean squared error in noisy ridgeless linear regression.
result Lower bound implies ridgeless regression performs poorly near interpolation threshold.
Semantic segmentation maps can be used as input to models for maneuvering the controls of a car. However, not all labels may be necessary for making the control decision. One would expect that certain labels such as road lanes or sidewalks would be more critical in comparison with labels for vegetation or buildings whi…
Visualizes classification results with class maps.
problem Label bias in classification predictions.
method Class maps reflecting probability, distance, and mislabeling likelihood.
result Insight into classification results and data structure.
Generates synthetic laparoscopic images for training deep neural networks.
problem Lack of large labeled data sets for laparoscopic image processing.
method Unpaired image-to-image translation to generate realistic synthetic images.
result Synthetic data set improves liver segmentation performance without manual labeling.
Many modern applications deal with multi-label data, such as functional categorizations of genes, image labeling and text categorization. Classification of such data with a large number of labels and latent dependencies among them is a challenging task, and it becomes even more challenging when the data is received onl…
The paper uses CNNs to classify road conditions in real-time.
problem Classifying road conditions in real-time using images from cameras across North America.
method Used state-of-the-art convolutional neural networks (VGG-16, ResNet50, Xception, InceptionResNetV2, EfficientNet-B0 and EfficientNet-B4) to classify images by road condition.
result EfficientNet-B4 framework achieved validation accuracy of 90.6% in real-time map building.
Self-distillation improves model performance by increasing teacher diversity and smoothing predictions.
problem Improving model generalization and performance through self-distillation.
method Interpreting self-distillation as MAP estimation and proposing instance-specific label smoothing.
result Self-distillation enhances model performance by increasing teacher diversity and smoothing predictions.
As sound event classification moves towards larger datasets, issues of label noise become inevitable. Web sites can supply large volumes of user-contributed audio and metadata, but inferring labels from this metadata introduces errors due to unreliable inputs, and limitations in the mapping. There is, however, little r…
Paper presents an audiovisual model to recognize sounds from weakly labeled video data.
problem Sound recognition from weakly labeled video data.
method Audiovisual fusion model with attention mechanism.
result The model achieves a mean Average Precision (mAP) of 46.16 on AudioSet, outperforming state-of-the-art models.
TASFAR adapts regression models without labeled source data.
problem Lack of labeled source data for domain adaptation.
method Uses prediction confidence to estimate target label distribution and calibrate source model.
result Substantially reduces errors in various regression tasks.
A new model improves semi-supervised learning for unstructured data.
problem Manual labeling of unstructured data is expensive, time-consuming, and prone to errors.
method Batch Semi-Supervised Self-Organizing Map (Batch SS-SOM) integrating deep learning techniques.
result Batch SS-SOM performs well in semi-supervised classification and clustering, even with few labeled samples.
We study the periods mapping from the moduli space of real hyperelliptic curves with marked point on an oriented oval to the euclidean space. The mapping arises in the analysis of Chebyshev construction used in the constrained optimization of the uniform norm of polynomials and rational functions. The decomposition of …
A new model embeds word and label hierarchies in hyperbolic space for HMLC.
problem Learning mappings from word hierarchies to label hierarchies in hierarchical multi-label classification.
method Proposes a Hyperbolic Interaction Model (HyperIM) to learn label-aware document representations in hyperbolic space.
result Demonstrates improved performance for HMLC compared to state-of-the-art methods.
In this paper, we propose an extension to an existing algorithm (instance-MIR) which tackles the multiple instance regression (MIR) problem, also known as distribution regression. The MIR setting arises when the data is a collection of bags, where each bag consists of several instances which correspond to the same and …
CCVAE captures label characteristics in VAEs for better representation learning.
problem Capturing rich label characteristics in VAEs without conflating them with label values.
method Developed CCVAE, a novel VAE model that explicitly captures label characteristics in latent space.
result CCVAE allows for effective and general interventions like smooth traversals and diverse conditional generation.
Crowdsourcing is an effective tool for human-powered computation on many tasks challenging for computers. In this paper, we provide finite-sample exponential bounds on the error rate (in probability and in expectation) of hyperplane binary labeling rules under the Dawid-Skene crowdsourcing model. The bounds can be appl…
Enhances graph classification models on small datasets.
problem Over-fitting and undergeneralization on small-scale benchmark datasets.
method Data augmentation via graph structure transformation and model evolution framework.
result Average improvement of 3 - 13% accuracy on graph classification tasks.
GraphXCOVID uses deep semi-supervised learning to identify COVID-19 from chest X-rays with minimal labels.
problem Identifying COVID-19 on chest X-rays with limited labelled data.
method Graph-based deep semi-supervised framework with pseudo-labeling and attention maps.
result Outperforms current supervised models with a tiny fraction of labelled examples.
In machine learning the best performance on a certain task is achieved by fully supervised methods when perfect ground truth labels are available. However, labels are often noisy, especially in remote sensing where manually curated public datasets are rare. We study the multi-modal cadaster map alignment problem for wh…
A new criterion for deep active learning selects minimal labeled data points.
problem Efficiently select minimal labeled data points for deep neural networks.
method Diffuses label information over a graph of data representations to switch between exploration and refinement.
result The diffusion-based criterion outperforms existing methods in deep active learning.
Paper tackles instance-dependent label noise by approximating it with part-dependent noise.
problem Learning with instance-dependent label noise is challenging.
method Approximate instance-dependent label noise with part-dependent noise. Use transition matrices for parts to model noise.
result Method outperforms state-of-the-art approaches for instance-dependent label noise.
Crowdsourcing has become an effective and popular tool for human-powered computation to label large datasets. Since the workers can be unreliable, it is common in crowdsourcing to assign multiple workers to one task, and to aggregate the labels in order to obtain results of high quality. In this paper, we provide finit…
Label assignment problems with large state spaces are important tasks especially in computer vision. Often the pairwise interaction (or smoothness prior) between labels assigned at adjacent nodes (or pixels) can be described as a function of the label difference. Exact inference in such labeling tasks is still difficul…
Multi-label learning is concerned with the classification of data with multiple class labels. This is in contrast to the traditional classification problem where every data instance has a single label. Due to the exponential size of output space, exploiting intrinsic information in feature and label spaces has been the…
We derive a new variational principle, leading to a new momentum map and a new multisymplectic formulation for a family of Euler--Poincaré equations defined on the Virasoro-Bott group, by using the inverse map (also called `back-to-labels' map). This family contains as special cases the well-known Korteweg-de Vries, Ca…
Proposes a method to improve weakly supervised image segmentation using deep geodesic priors.
problem Limited availability of high-quality annotations for image segmentation, especially in medical data.
method Integrates a deep geodesic prior extracted from an auto-encoder to reduce the adverse effects of weak labels in segmentation accuracy.
result The proposed method significantly improves segmentation accuracy, boosting performance by 4.4% in dice score for clean labels and up to 6.3% for noisy labels (L2).
A method to approximate instance-dependent label noise using instance-confidence embedding.
problem Real-world label noise that depends on individual instances.
method Variational approximation with instance embedding to capture instance-specific label corruption.
result ICE method effectively approximates instance-dependent noise and detects ambiguous instances.
This paper proposes synthetic augmentation for nuclei image segmentation in medical pathology.
problem Rare and time-consuming labeling of tumor nuclei images for semantic segmentation.
method Label-to-image translation to generate synthetic images.
result Synthetic augmentation improves segmentation accuracy.
In multi-label learning, each sample is associated with several labels. Existing works indicate that exploring correlations between labels improve the prediction performance. However, embedding the label correlations into the training process significantly increases the problem size. Moreover, the mapping of the label …