Papers Model Optimization
“Model Optimization” 태그가 달린 논문 380편 · 필터 해제
Development, Optimization, and Deployment of Thermal Forward Vision Systems for Advance Vehicular Applications on Edge Devices
In this research work, we have proposed a thermal tiny-YOLO multi-class object detection (TTYMOD) system as a smart forward sensing system that should remain effective in all weather and harsh environmental conditions us…
Model Optimizationobject-detectionObject DetectionQuantization+1Multi-scale multi-modal micro-expression recognition algorithm based on transformer
A micro-expression is a spontaneous unconscious facial muscle movement that can reveal the true emotions people attempt to hide. Although manual methods have made good progress and deep learning is gaining prominence. Du…
Contrastive LearningMicro Expression RecognitionMicro-Expression RecognitionModel OptimizationOn the Difficulty of Unpaired Infrared-to-Visible Video Translation: Fine-Grained Content-Rich Patches Transfer
Explicit visible videos can provide sufficient visual information and facilitate vision applications. Unfortunately, the image sensors of visible cameras are sensitive to light conditions like darkness or overexposur…
Autonomous DrivingModel OptimizationAdjustment and Alignment for Unbiased Open Set Domain Adaptation
Open Set Domain Adaptation (OSDA) transfers the model from a label-rich domain to a label-free one containing novel-class samples. Existing OSDA works overlook abundant novel-class semantics hidden in the source doma…
Domain AdaptationModel OptimizationExploring the Optimized Value of Each Hyperparameter in Various Gradient Descent Algorithms
In the recent years, various gradient descent algorithms including the methods of gradient descent, gradient descent with momentum, adaptive gradient (AdaGrad), root-mean-square propagation (RMSProp) and adaptive moment …
Model OptimizationMulti-Scales Data Augmentation Approach In Natural Language Inference For Artifacts Mitigation And Pre-Trained Model Optimization
Machine learning models can reach high performance on benchmark natural language processing (NLP) datasets but fail in more challenging settings. We study this issue when a pre-trained model learns dataset artifacts in n…
Data AugmentationModel OptimizationNatural Language InferenceSentenceCbwLoss: Constrained Bidirectional Weighted Loss for Self-supervised Learning of Depth and Pose
Photometric differences are widely used as supervision signals to train neural networks for estimating depth and camera pose from unlabeled monocular videos. However, this approach is detrimental for model optimization b…
Model OptimizationSelf-Supervised LearningA Node-collaboration-informed Graph Convolutional Network for Precise Representation to Undirected Weighted Graphs
An undirected weighted graph (UWG) is frequently adopted to describe the interactions among a solo set of nodes from real applications, such as the user contact frequency from a social network services system. A graph co…
Model OptimizationRepresentation LearningT-SEA: Transfer-based Self-Ensemble Attack on Object Detection
Compared to query-based black-box attacks, transfer-based black-box attacks do not require any information of the attacked models, which ensures their secrecy. However, most existing transfer-based approaches rely on ens…
Adversarial AttackModel Optimizationobject-detectionObject DetectionDomain-incremental Cardiac Image Segmentation with Style-oriented Replay and Domain-sensitive Feature Whitening
Contemporary methods have shown promising results on cardiac image segmentation, but merely in static learning, i.e., optimizing the network once for all, ignoring potential needs for model updating. In real-world scenar…
Image SegmentationIncremental LearningModel OptimizationSemantic SegmentationOn the Domain Adaptation and Generalization of Pretrained Language Models: A Survey
Recent advances in NLP are brought by a range of large-scale pretrained language models (PLMs). These PLMs have brought significant performance gains for a range of NLP tasks, circumventing the need to customize complex …
Domain AdaptationModel OptimizationMitigating Popularity Bias in Recommendation with Unbalanced Interactions: A Gradient Perspective
Recommender systems learn from historical user-item interactions to identify preferred items for target users. These observed interactions are usually unbalanced following a long-tailed distribution. Such long-tailed dat…
Model OptimizationRecommendation SystemsStreaming Radiance Fields for 3D Video Synthesis
We present an explicit-grid based method for efficiently reconstructing streaming radiance fields for novel view synthesis of real world dynamic scenes. Instead of training a single model that combines all the frames, we…
Incremental LearningModel OptimizationNovel View SynthesisImproving Adversarial Robustness with Self-Paced Hard-Class Pair Reweighting
Deep Neural Networks are vulnerable to adversarial attacks. Among many defense strategies, adversarial training with untargeted attacks is one of the most effective methods. Theoretically, adversarial perturbation in unt…
Adversarial RobustnessModel OptimizationSemantic SimilaritySemantic Textual SimilarityA New Perspective for Understanding Generalization Gap of Deep Neural Networks Trained with Large Batch Sizes
Deep neural networks (DNNs) are typically optimized using various forms of mini-batch gradient descent algorithm. A major motivation for mini-batch gradient descent is that with a suitably chosen batch size, available co…
Model OptimizationData-Augmented Counterfactual Learning for Bundle Recommendation
Bundle Recommendation (BR) aims at recommending bundled items on online content or e-commerce platform, such as song lists on a music platform or book lists on a reading website. Several graph based models have achieved …
counterfactualData AugmentationGraph LearningModel OptimizationControllable Style Transfer via Test-time Training of Implicit Neural Representation
We propose a controllable style transfer framework based on Implicit Neural Representation that pixel-wisely controls the stylized output via test-time training. Unlike traditional image optimization methods that often s…
Model OptimizationStyle TransferFloorplan-Aware Camera Poses Refinement
Processing large indoor scenes is a challenging task, as scan registration and camera trajectory estimation methods accumulate errors across time. As a result, the quality of reconstructed scans is insufficient for some …
3D ReconstructionModel OptimizationA deep learning network with differentiable dynamic programming for retina OCT surface segmentation
Multiple-surface segmentation in Optical Coherence Tomography (OCT) images is a challenge problem, further complicated by the frequent presence of weak image boundaries. Recently, many deep learning (DL) based methods ha…
Model OptimizationSegmentationAlphaFold Distillation for Protein Design
Inverse protein folding, the process of designing sequences that fold into a specific 3D structure, is crucial in bio-engineering and drug discovery. Traditional methods rely on experimentally resolved structures, but th…
DiversityDrug DiscoveryKnowledge DistillationModel Optimization+2