Search Results for author:
Found 11 papers, 4 papers with code
Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training
In this paper, we provide a causal inference framework to show that the correlative features caused by the target object and its co-occurring objects can be regarded as a mediator, which has both positive and negative impacts on model predictions.
Dirichlet-Based Prediction Calibration for Learning with Noisy Labels
Learning with noisy labels can significantly hinder the generalization performance of deep neural networks (DNNs).
Multi-Label Knowledge Distillation
Existing knowledge distillation methods typically work by imparting the knowledge of output logits or intermediate feature maps from the teacher network to the student network, which is very successful in multi-class single-label learning.
Unlocking the Power of Open Set : A New Perspective for Open-Set Noisy Label Learning
Learning from noisy data has attracted much attention, where most methods focus on closed-set label noise.
Class-Distribution-Aware Pseudo Labeling for Semi-Supervised Multi-Label Learning
Pseudo-labeling has emerged as a popular and effective approach for utilizing unlabeled data.
Noise-Robust Bidirectional Learning with Dynamic Sample Reweighting
Deep neural networks trained with standard cross-entropy loss are more prone to memorize noisy labels, which degrades their performance.
Meta Objective Guided Disambiguation for Partial Label Learning
In this paper, we propose a novel framework for partial label learning with meta objective guided disambiguation (MoGD), which aims to recover the ground-truth label from candidate labels set by solving a meta objective on a small validation set.
A Deep Model for Partial Multi-Label Image Classification with Curriculum Based Disambiguation
In this paper, we study the partial multi-label (PML) image classification problem, where each image is annotated with a candidate label set consists of multiple relevant labels and other noisy labels.
Multi-Label Learning with Pairwise Relevance Ordering
However, the supervised information of pairwise relevance ordering is less informative than exact labels.
CCMN: A General Framework for Learning with Class-Conditional Multi-Label Noise
Class-conditional noise commonly exists in machine learning tasks, where the class label is corrupted with a probability depending on its ground-truth.
Active Feature Acquisition with Supervised Matrix Completion
Feature missing is a serious problem in many applications, which may lead to low quality of training data and further significantly degrade the learning performance.
