Papers Model Optimization
“Model Optimization” 태그가 달린 논문 380편 · 필터 해제
FastLogAD: Log Anomaly Detection with Mask-Guided Pseudo Anomaly Generation and Discrimination
Nowadays large computers extensively output logs to record the runtime status and it has become crucial to identify any suspicious or malicious activities from the information provided by the realtime logs. Thus, fast lo…
Anomaly DetectionModel OptimizationEdge-Efficient Deep Learning Models for Automatic Modulation Classification: A Performance Analysis
The recent advancement in deep learning (DL) for automatic modulation classification (AMC) of wireless signals has encouraged numerous possible applications on resource-constrained edge devices. However, developing optim…
Knowledge DistillationModel OptimizationQuantizationForecasting the Future with Future Technologies: Advancements in Large Meteorological Models
The field of meteorological forecasting has undergone a significant transformation with the integration of large models, especially those employing deep learning techniques. This paper reviews the advancements and applic…
Model OptimizationPredictionUrban Architect: Steerable 3D Urban Scene Generation with Layout Prior
Text-to-3D generation has achieved remarkable success via large-scale text-to-image diffusion models. Nevertheless, there is no paradigm for scaling up the methodology to urban scale. Urban scenes, characterized by numer…
3D GenerationModel OptimizationScene GenerationText to 3DExhaustive Exploitation of Nature-inspired Computation for Cancer Screening in an Ensemble Manner
Accurate screening of cancer types is crucial for effective cancer detection and precise treatment selection. However, the association between gene expression profiles and tumors is often limited to a small number of bio…
Cancer ClassificationDiversityEnsemble Learningfeature selection+1GLCM-Based Feature Combination for Extraction Model Optimization in Object Detection Using Machine Learning
In the era of modern technology, object detection using the Gray Level Co-occurrence Matrix (GLCM) extraction method plays a crucial role in object recognition processes. It finds applications in real-time scenarios such…
Computational EfficiencyModel OptimizationObjectobject-detection+3Diffusion Deepfake
Recent progress in generative AI, primarily through diffusion models, presents significant challenges for real-world deepfake detection. The increased realism in image details, diverse content, and widespread accessibili…
DeepFake DetectionDiversityFace SwappingModel OptimizationThe LSCD Benchmark: a Testbed for Diachronic Word Meaning Tasks
Lexical Semantic Change Detection (LSCD) is a complex, lemma-level task, which is usually operationalized based on two subsequently applied usage-level tasks: First, Word-in-Context (WiC) labels are derived for pairs of …
Change DetectionLEMMAModel OptimizationWord Sense InductionBoosting Diffusion Models with Moving Average Sampling in Frequency Domain
Diffusion models have recently brought a powerful revolution in image generation. Despite showing impressive generative capabilities, most of these models rely on the current sample to denoise the next one, possibly resu…
DenoisingImage GenerationModel OptimizationAD-NEv++ : The multi-architecture neuroevolution-based multivariate anomaly detection framework
Anomaly detection tools and methods enable key analytical capabilities in modern cyberphysical and sensor-based systems. Despite the fast-paced development in deep learning architectures for anomaly detection, model opti…
Anomaly DetectionModel OptimizationFew-shot Object Localization
Existing object localization methods are tailored to locate specific classes of objects, relying heavily on abundant labeled data for model optimization. However, acquiring large amounts of labeled data is challenging in…
Model OptimizationObjectObject CountingObject LocalizationMonkeypox disease recognition model based on improved SE-InceptionV3
In the wake of the global spread of monkeypox, accurate disease recognition has become crucial. This study introduces an improved SE-InceptionV3 model, embedding the SENet module and incorporating L2 regularization into …
L2 RegularizationModel OptimizationSelf-Supervised Learning for Time Series: Contrastive or Generative?
Self-supervised learning (SSL) has recently emerged as a powerful approach to learning representations from large-scale unlabeled data, showing promising results in time series analysis. The self-supervised representatio…
Model OptimizationRepresentation LearningSelf-Supervised LearningTime Series+1ProMoAI: Process Modeling with Generative AI
ProMoAI is a novel tool that leverages Large Language Models (LLMs) to automatically generate process models from textual descriptions, incorporating advanced prompt engineering, error handling, and code generation techn…
Code GenerationModel OptimizationPrompt EngineeringEmbedded Multi-label Feature Selection via Orthogonal Regression
In the last decade, embedded multi-label feature selection methods, incorporating the search for feature subsets into model optimization, have attracted considerable attention in accurately evaluating the importance of f…
feature selectionModel OptimizationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Flattening Singular Values of Factorized Convolution for Medical Images
Convolutional neural networks (CNNs) have long been the paradigm of choice for robust medical image processing (MIP). Therefore, it is crucial to effectively and efficiently deploy CNNs on devices with different computin…
Model OptimizationOptimizing Language Models for Human Preferences is a Causal Inference Problem
As large language models (LLMs) see greater use in academic and commercial settings, there is increasing interest in methods that allow language models to generate texts aligned with human preferences. In this paper, we …
Causal InferenceLanguage ModelingLanguage ModellingModel OptimizationAds Recommendation in a Collapsed and Entangled World
We present Tencent's ads recommendation system and examine the challenges and practices of learning appropriate recommendation representations. Our study begins by showcasing our approaches to preserving prior knowledge …
Feature CorrelationModel OptimizationMetaSeg: Content-Aware Meta-Net for Omni-Supervised Semantic Segmentation
Noisy labels, inevitably existing in pseudo segmentation labels generated from weak object-level annotations, severely hampers model optimization for semantic segmentation. Previous works often rely on massive hand-craft…
Meta-LearningModel OptimizationSegmentationSemantic SegmentationAdaptive Global-Local Representation Learning and Selection for Cross-Domain Facial Expression Recognition
Domain shift poses a significant challenge in Cross-Domain Facial Expression Recognition (CD-FER) due to the distribution variation across different domains. Current works mainly focus on learning domain-invariant featur…
Cross-Domain Facial Expression RecognitionFacial Expression RecognitionModel OptimizationPseudo Label+1