Search Results for author:
Found 32 papers, 11 papers with code
Cost-effectively Identifying Causal Effect When Only Response Variable Observable
In many real tasks, we care about how to make decisions other than mere predictions on an event, e. g. how to increase the revenue next month instead of knowing it will drop.
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.
Bidirectional Uncertainty-Based Active Learning for Open Set Annotation
In this paper, we attempt to query examples that are both likely from known classes and highly informative, and propose a \textit{Bidirectional Uncertainty-based Active Learning} (BUAL) framework.
Empowering Language Models with Active Inquiry for Deeper Understanding
The rise of large language models (LLMs) has revolutionized the way that we interact with artificial intelligence systems through natural language.
Dirichlet-Based Prediction Calibration for Learning with Noisy Labels
Learning with noisy labels can significantly hinder the generalization performance of deep neural networks (DNNs).
Improving Lens Flare Removal with General Purpose Pipeline and Multiple Light Sources Recovery
In this paper, we propose a solution to improve the performance of lens flare removal by revisiting the ISP and remodeling the principle of automatic exposure in the synthesis pipeline and design a more reliable light sources recovery strategy.
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.
ALL-E: Aesthetics-guided Low-light Image Enhancement
In this paper, we propose a new paradigm, i. e., aesthetics-guided low-light image enhancement (ALL-E), which introduces aesthetic preferences to LLE and motivates training in a reinforcement learning framework with an aesthetic reward.
Implicit Stochastic Gradient Descent for Training Physics-informed Neural Networks
Physics-informed neural networks (PINNs) have effectively been demonstrated in solving forward and inverse differential equation problems, but they are still trapped in training failures when the target functions to be approximated exhibit high-frequency or multi-scale features.
Improving Lens Flare Removal with General-Purpose Pipeline and Multiple Light Sources Recovery
In this paper, we propose a solution to improve the performance of lens flare removal by revisiting the ISP and remodeling the principle of automatic exposure in the synthesis pipeline and design a more reliable light sources recovery strategy.
MUS-CDB: Mixed Uncertainty Sampling with Class Distribution Balancing for Active Annotation in Aerial Object Detection
However, existing active learning methods are mainly with class-balanced settings and image-based querying for generic object detection tasks, which are less applicable to aerial object detection scenarios due to the long-tailed class distribution and dense small objects in aerial scenes.
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.
Can Adversarial Training Be Manipulated By Non-Robust Features?
Under this threat, we show that adversarial training using a conventional defense budget $\epsilon$ provably fails to provide test robustness in a simple statistical setting, where the non-robust features of the training data can be reinforced by $\epsilon$-bounded perturbation.
Active Learning for Open-set Annotation
To tackle this open-set annotation (OSA) problem, we propose a new active learning framework called LfOSA, which boosts the classification performance with an effective sampling strategy to precisely detect examples from known classes for annotation.
Multi-Label Learning with Pairwise Relevance Ordering
However, the supervised information of pairwise relevance ordering is less informative than exact labels.
Learning from Crowds with Sparse and Imbalanced Annotations
Traditional supervised learning requires ground truth labels for the training data, whose collection can be difficult in many cases.
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.
Co-Imitation Learning without Expert Demonstration
Imitation learning is a primary approach to improve the efficiency of reinforcement learning by exploiting the expert demonstrations.
Improving Model Robustness by Adaptively Correcting Perturbation Levels with Active Queries
Recently, much research has been devoted to improving the model robustness by training with noise perturbations.
Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial Training
Delusive attacks aim to substantially deteriorate the test accuracy of the learning model by slightly perturbing the features of correctly labeled training examples.
Reinforcement Learning with Supervision from Noisy Demonstrations
In this paper, we propose a novel framework to adaptively learn the policy by jointly interacting with the environment and exploiting the expert demonstrations.
ALiPy: Active Learning in Python
Supervised machine learning methods usually require a large set of labeled examples for model training.
Recent Advances in Open Set Recognition: A Survey
A more realistic scenario is open set recognition (OSR), where incomplete knowledge of the world exists at training time, and unknown classes can be submitted to an algorithm during testing, requiring the classifiers to not only accurately classify the seen classes, but also effectively deal with the unseen ones.
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.
Cost-Effective Training of Deep CNNs with Active Model Adaptation
Deep convolutional neural networks have achieved great success in various applications.
WoCE: a framework for clustering ensemble by exploiting the wisdom of Crowds theory
We employ four conditions in the WOC theory, i. e., diversity, independency, decentralization and aggregation, to guide both the constructing of individual clustering results and the final combination for clustering ensemble.
Fast Multi-Instance Multi-Label Learning
Although the MIML problem is complicated, MIMLfast is able to achieve excellent performance by exploiting label relations with shared space and discovering sub-concepts for complicated labels.
Active Learning by Querying Informative and Representative Examples
Most active learning approaches select either informative or representative unlabeled instances to query their labels.
