Papers Model Optimization
“Model Optimization” 태그가 달린 논문 380편 · 필터 해제
Invariant Aggregator for Defending against Federated Backdoor Attacks
Federated learning enables training high-utility models across several clients without directly sharing their private data. As a downside, the federated setting makes the model vulnerable to various adversarial attacks i…
Federated LearningModel OptimizationGenerating Synthetic Data with Locally Estimated Distributions for Disclosure Control
Sensitive datasets are often underutilized in research and industry due to privacy concerns, limiting the potential of valuable data-driven insights. Synthetic data generation presents a promising solution to address thi…
ClusteringHyperparameter OptimizationImputationModel Optimization+1Device-friendly Guava fruit and leaf disease detection using deep learning
This work presents a deep learning-based plant disease diagnostic system using images of fruits and leaves. Five state-of-the-art convolutional neural networks (CNN) have been employed for implementing the system. Hither…
DiagnosticModel OptimizationQuantizationGrouped Adaptive Loss Weighting for Person Search
Person search is an integrated task of multiple sub-tasks such as foreground/background classification, bounding box regression and person re-identification. Therefore, person search is a typical multi-task learning prob…
Model OptimizationMulti-Task LearningPerson Re-IdentificationPerson SearchPicT: A Slim Weakly Supervised Vision Transformer for Pavement Distress Classification
Automatic pavement distress classification facilitates improving the efficiency of pavement maintenance and reducing the cost of labor and resources. A recently influential branch of this task divides the pavement image …
image-classificationImage ClassificationModel OptimizationTowards Optimization and Model Selection for Domain Generalization: A Mixup-guided Solution
The distribution shifts between training and test data typically undermine the performance of models. In recent years, lots of work pays attention to domain generalization (DG) where distribution shifts exist, and target…
Domain GeneralizationModel OptimizationModel SelectionTime Series+1Archangel: A Hybrid UAV-based Human Detection Benchmark with Position and Pose Metadata
Learning to detect objects, such as humans, in imagery captured by an unmanned aerial vehicle (UAV) usually suffers from tremendous variations caused by the UAV's position towards the objects. In addition, existing UAV-b…
Human DetectionModel OptimizationObjectobject-detection+2Tensor Decomposition based Personalized Federated Learning
Federated learning (FL) is a new distributed machine learning framework that can achieve reliably collaborative training without collecting users' private data. However, due to FL's frequent communication and average agg…
DiversityFederated LearningModel OptimizationPersonalized Federated Learning+1Automatic reorientation by deep learning to generate short axis SPECT myocardial perfusion images
Single photon emission computed tomography (SPECT) myocardial perfusion images (MPI) can be displayed both in traditional short-axis (SA) cardiac planes and polar maps for interpretation and quantification. It is essenti…
Model OptimizationTranslationNeighborhood Collective Estimation for Noisy Label Identification and Correction
Learning with noisy labels (LNL) aims at designing strategies to improve model performance and generalization by mitigating the effects of model overfitting to noisy labels. The key success of LNL lies in identifying as …
Learning with noisy labelsModel OptimizationExtraction of Vascular Wall in Carotid Ultrasound via a Novel Boundary-Delineation Network
Ultrasound imaging plays an important role in the diagnosis of vascular lesions. Accurate segmentation of the vascular wall is important for the prevention, diagnosis and treatment of vascular diseases. However, existing…
Model OptimizationSegmentationSegment-level Metric Learning for Few-shot Bioacoustic Event Detection
Few-shot bioacoustic event detection is a task that detects the occurrence time of a novel sound given a few examples. Previous methods employ metric learning to build a latent space with the labeled part of different so…
Event DetectionFew-Shot LearningMetric LearningModel OptimizationQSAN: A Near-term Achievable Quantum Self-Attention Network
Self-Attention Mechanism (SAM) is good at capturing the internal connections of features and greatly improves the performance of machine learning models, espeacially requiring efficient characterization and feature extra…
Binary Classificationimage-classificationImage ClassificationModel Optimization+2Mix-Teaching: A Simple, Unified and Effective Semi-Supervised Learning Framework for Monocular 3D Object Detection
Monocular 3D object detection is an essential perception task for autonomous driving. However, the high reliance on large-scale labeled data make it costly and time-consuming during model optimization. To reduce such ove…
3D Object DetectionAutonomous DrivingModel OptimizationMonocular 3D Object Detection+2An Embedding-Dynamic Approach to Self-supervised Learning
A number of recent self-supervised learning methods have shown impressive performance on image classification and other tasks. A somewhat bewildering variety of techniques have been used, not always with a clear understa…
Classificationimage-classificationImage ClassificationInstance Segmentation+6Turning to a Teacher for Timestamp Supervised Temporal Action Segmentation
Temporal action segmentation in videos has drawn much attention recently. Timestamp supervision is a cost-effective way for this task. To obtain more information to optimize the model, the existing method generated pseud…
Action SegmentationModel OptimizationSegmentationTemporal Action SegmentationFL-Tuning: Layer Tuning for Feed-Forward Network in Transformer
Prompt tuning is an emerging way of adapting pre-trained language models to downstream tasks. However, the existing studies are mainly to add prompts to the input sequence. This way would not work as expected due to the …
Model OptimizationA Systematic Comparison of Phonetic Aware Techniques for Speech Enhancement
Speech enhancement has seen great improvement in recent years using end-to-end neural networks. However, most models are agnostic to the spoken phonetic content. Recently, several studies suggested phonetic-aware speech …
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Model OptimizationSelf-Supervised Learning+2Model Optimization in Imbalanced Regression
Imbalanced domain learning aims to produce accurate models in predicting instances that, though underrepresented, are of utmost importance for the domain. Research in this field has been mainly focused on classification …
modelModel OptimizationregressionExtending Process Discovery with Model Complexity Optimization and Cyclic States Identification: Application to Healthcare Processes
Within Process mining, discovery techniques had made it possible to construct business process models automatically from event logs. However, results often do not achieve the balance between model complexity and its fitt…
Model Optimization