Papers Model Optimization
“Model Optimization” 태그가 달린 논문 380편 · 필터 해제
Meta-Learning for Few-Shot Land Cover Classification
The representations of the Earth's surface vary from one geographic region to another. For instance, the appearance of urban areas differs between continents, and seasonality influences the appearance of vegetation. To c…
ClassificationDiversityGeneral ClassificationLand Cover Classification+3Dynamic Scale Training for Object Detection
We propose a Dynamic Scale Training paradigm (abbreviated as DST) to mitigate scale variation challenge in object detection. Previous strategies like image pyramid, multi-scale training, and their variants are aiming at …
Instance SegmentationModel OptimizationObjectobject-detection+2A Fast Fully Octave Convolutional Neural Network for Document Image Segmentation
The Know Your Customer (KYC) and Anti Money Laundering (AML) are worldwide practices to online customer identification based on personal identification documents, similarity and liveness checking, and proof of address. T…
Image SegmentationModel OptimizationSegmentationSemantic Segmentation+1Monocular Human Pose and Shape Reconstruction using Part Differentiable Rendering
Superior human pose and shape reconstruction from monocular images depends on removing the ambiguities caused by occlusions and shape variance. Recent works succeed in regression-based methods which estimate parametric m…
3D Human Pose Estimation3D Pose EstimationModel OptimizationPose Estimation+2ML4Chem: A Machine Learning Package for Chemistry and Materials Science
ML4Chem is an open-source machine learning library for chemistry and materials science. It provides an extendable platform to develop and deploy machine learning models and pipelines and is targeted to the non-expert and…
BIG-bench Machine LearningModel OptimizationregressionComprehensive and Efficient Data Labeling via Adaptive Model Scheduling
Labeling data (e.g., labeling the people, objects, actions and scene in images) comprehensively and efficiently is a widely needed but challenging task. Numerous models were proposed to label various data and many approa…
Deep Reinforcement LearningImage RetrievalManagementModel Optimization+3Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Results
Deep learning models have shown their advantage in many different tasks, including neuroimage analysis. However, to effectively train a high-quality deep learning model, the aggregation of a significant amount of patient…
Domain AdaptationFederated LearningModel OptimizationPrivacy PreservingAutoScale: Learning to Scale for Crowd Counting and Localization
Recent works on crowd counting mainly leverage CNNs to count by regressing density maps, and have achieved great progress. In the density map, each person is represented by a Gaussian blob, and the final count is obtaine…
Crowd CountingModel OptimizationPrediction of individual progression rate in Parkinson's disease using clinical measures and biomechanical measures of gait and postural stability
Parkinson's disease (PD) is a common neurological disorder characterized by gait impairment. PD has no cure, and an impediment to developing a treatment is the lack of any accepted method to predict disease progression r…
BIG-bench Machine LearningModel OptimizationVisual Tactile Fusion Object Clustering
Object clustering, aiming at grouping similar objects into one cluster with an unsupervised strategy, has been extensivelystudied among various data-driven applications. However, most existing state-of-the-art object clu…
ClusteringModel OptimizationObjectERNet Family: Hardware-Oriented CNN Models for Computational Imaging Using Block-Based Inference
Convolutional neural networks (CNNs) demand huge DRAM bandwidth for computational imaging tasks, and block-based processing has recently been applied to greatly reduce the bandwidth. However, the induced additional compu…
4kDenoisingImage DenoisingModel Optimization+1Flood Prediction Using Machine Learning Models: Literature Review
Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models contributed to risk reduction, policy suggestion, minimization of the…
BIG-bench Machine LearningModel OptimizationPredictionAcoustic Model Optimization Based On Evolutionary Stochastic Gradient Descent with Anchors for Automatic Speech Recognition
Evolutionary stochastic gradient descent (ESGD) was proposed as a population-based approach that combines the merits of gradient-aware and gradient-free optimization algorithms for superior overall optimization performan…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Model Optimizationspeech-recognition+1Variational Inference for Sparse Gaussian Process Modulated Hawkes Process
The Hawkes process (HP) has been widely applied to modeling self-exciting events including neuron spikes, earthquakes and tweets. To avoid designing parametric triggering kernel and to be able to quantify the prediction …
Model OptimizationModel SelectionVariational InferenceSemi-supervised and Population Based Training for Voice Commands Recognition
We present a rapid design methodology that combines automated hyper-parameter tuning with semi-supervised training to build highly accurate and robust models for voice commands classification. Proposed approach allows qu…
ClassificationGeneral ClassificationModel OptimizationPerformance Optimization on Model Synchronization in Parallel Stochastic Gradient Descent Based SVM
Understanding the bottlenecks in implementing stochastic gradient descent (SGD)-based distributed support vector machines (SVM) algorithm is important in training larger data sets. The communication time to do the model …
Model OptimizationA deep learning based solution for construction equipment detection: from development to deployment
This paper aims at providing researchers and engineering professionals with a practical and comprehensive deep learning based solution to detect construction equipment from the very first step of its development to the l…
Model OptimizationModel Selectionobject-detectionObject DetectionMinimum Volume Topic Modeling
We propose a new topic modeling procedure that takes advantage of the fact that the Latent Dirichlet Allocation (LDA) log likelihood function is asymptotically equivalent to the logarithm of the volume of the topic simpl…
Model OptimizationVideo Relationship Reasoning using Gated Spatio-Temporal Energy Graph
Visual relationship reasoning is a crucial yet challenging task for understanding rich interactions across visual concepts. For example, a relationship 'man, open, door' involves a complex relation 'open' between concret…
Model OptimizationVideo RelationshipVideo Visual Relation DetectionRepresentative Task Self-selection for Flexible Clustered Lifelong Learning
Consider the lifelong machine learning paradigm whose objective is to learn a sequence of tasks depending on previous experiences, e.g., knowledge library or deep network weights. However, the knowledge libraries or deep…
Lifelong learningModel OptimizationMulti-Task Learning