Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
Taming Pretrained Transformers for Extreme Multi-label Text Classification
We consider the extreme multi-label text classification (XMC) problem: given an input text, return the most relevant labels from a large label collection. For example, the input text could be a product description on Ama…
Extreme Multi-Label ClassificationGeneral ClassificationMulti-Label ClassificationMulti-Label Text Classification+4Distinguishability of Adversarial Examples
Machine learning models including traditional models and neural networks can be easily fooled by adversarial examples which are generated from the natural examples with small perturbations. This poses a critical challen…
BIG-bench Machine LearningMalware DetectionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONAttentive Task-Agnostic Meta-Learning for Few-Shot Text Classification
Current deep learning based text classification methods are limited by their ability to achieve fast learning and generalization when the data is scarce. We address this problem by integrating a meta-learning procedure t…
ClassificationFew-Shot Text ClassificationGeneral ClassificationInductive Bias+8A CNN-RNN Architecture for Multi-Label Weather Recognition
Weather Recognition plays an important role in our daily lives and many computer vision applications. However, recognizing the weather conditions from a single image remains challenging and has not been studied thoroughl…
General ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONML-KFHE: Multi-label ensemble classification algorithm exploiting sensor fusion properties of the Kalman filter
Despite the success of ensemble classification methods in multi-class classification problems, ensemble methods based on approaches other than bagging have not been widely explored for multi-label classification problems…
General ClassificationMulti-class ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1CascadeML: An Automatic Neural Network Architecture Evolution and Training Algorithm for Multi-label Classification
Multi-label classification is an approach which allows a datapoint to be labelled with more than one class at the same time. A common but trivial approach is to train individual binary classifiers per label, but the perf…
AutoMLGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONReliable Multi-label Classification: Prediction with Partial Abstention
In contrast to conventional (single-label) classification, the setting of multilabel classification (MLC) allows an instance to belong to several classes simultaneously. Thus, instead of selecting a single class label, p…
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Bonsai -- Diverse and Shallow Trees for Extreme Multi-label Classification
Extreme multi-label classification (XMC) refers to supervised multi-label learning involving hundreds of thousand or even millions of labels. In this paper, we develop a suite of algorithms, called Bonsai, which generali…
ClassificationExtreme Multi-Label ClassificationGeneral ClassificationMulti-Label Classification+2Neural Message Passing for Multi-Label Classification
Multi-label classification (MLC) is the task of assigning a set of target labels for a given sample. Modeling the combinatorial label interactions in MLC has been a long-haul challenge. We propose Label Message Passing (…
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONAdapting RNN Sequence Prediction Model to Multi-label Set Prediction
We present an adaptation of RNN sequence models to the problem of multi-label classification for text, where the target is a set of labels, not a sequence. Previous such RNN models define probabilities for sequences but …
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONPredictionElucidating image-to-set prediction: An analysis of models, losses and datasets
In this paper, we identify an important reproducibility challenge in the image-to-set prediction literature that impedes proper comparisons among published methods, namely, researchers use different evaluation protocols …
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONPredictionRanking-Based Autoencoder for Extreme Multi-label Classification
Extreme Multi-label classification (XML) is an important yet challenging machine learning task, that assigns to each instance its most relevant candidate labels from an extremely large label collection, where the numbers…
ClassificationExtreme Multi-Label ClassificationFeature ImportanceGeneral Classification+2Multi-View Matrix Completion for Multi-Label Image Classification
There is growing interest in multi-label image classification due to its critical role in web-based image analytics-based applications, such as large-scale image retrieval and browsing. Matrix completion has recently bee…
ClassificationGeneral Classificationimage-classificationImage Classification+6$\mathcal{G}$-softmax: Improving Intra-class Compactness and Inter-class Separability of Features
Intra-class compactness and inter-class separability are crucial indicators to measure the effectiveness of a model to produce discriminative features, where intra-class compactness indicates how close the features with …
General ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label Image Recognition with Graph Convolutional Networks
The task of multi-label image recognition is to predict a set of object labels that present in an image. As objects normally co-occur in an image, it is desirable to model the label dependencies to improve the recognitio…
Long-tail LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label Image Recognition+2Comparison of State-of-the-Art Deep Learning APIs for Image Multi-Label Classification using Semantic Metrics
Image understanding heavily relies on accurate multi-label classification. In recent years, deep learning algorithms have become very successful for such tasks, and various commercial and open-source APIs have been relea…
General ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSemantic Similarity+1Imbalanced multi-label classification using multi-task learning with extractive summarization
Extractive summarization and imbalanced multi-label classification often require vast amounts of training data to avoid overfitting. In situations where training data is expensive to generate, leveraging information betw…
ClassificationExtractive SummarizationGeneral ClassificationMulti-Label Classification+3A Sequential Set Generation Method for Predicting Set-Valued Outputs
Consider a general machine learning setting where the output is a set of labels or sequences. This output set is unordered and its size varies with the input. Whereas multi-label classification methods seem a natural fir…
General ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONLearning a Deep ConvNet for Multi-label Classification with Partial Labels
Deep ConvNets have shown great performance for single-label image classification (e.g. ImageNet), but it is necessary to move beyond the single-label classification task because pictures of everyday life are inherently m…
ClassificationGeneral Classificationimage-classificationImage Classification+4Improving performance and inference on audio classification tasks using capsule networks
Classification of audio samples is an important part of many auditory systems. Deep learning models based on the Convolutional and the Recurrent layers are state-of-the-art in many such tasks. In this paper, we approach …
Audio ClassificationClassificationGeneral ClassificationMulti-Label Classification+2