Papers One-Shot Learning
“One-Shot Learning” 태그가 달린 논문 318편 · 필터 해제
Mathematics of Digital Twins and Transfer Learning for PDE Models
We define a digital twin (DT) of a physical system governed by partial differential equations (PDEs) as a model for real-time simulations and control of the system behavior under changing conditions. We construct DTs usi…
One-Shot LearningTransfer LearningVariable SelectionPsyDT: Using LLMs to Construct the Digital Twin of Psychological Counselor with Personalized Counseling Style for Psychological Counseling
Currently, large language models (LLMs) have made significant progress in the field of psychological counseling. However, existing mental health LLMs overlook a critical issue where they do not consider the fact that dif…
One-Shot LearningThe use of large language models to enhance cancer clinical trial educational materials
Cancer clinical trials often face challenges in recruitment and engagement due to a lack of participant-facing informational and educational resources. This study investigated the potential of Large Language Models (LLMs…
MisinformationMultiple-choiceOne-Shot LearningZero-Shot LearningFusion Matters: Learning Fusion in Deep Click-through Rate Prediction Models
The evolution of previous Click-Through Rate (CTR) models has mainly been driven by proposing complex components, whether shallow or deep, that are adept at modeling feature interactions. However, there has been less foc…
Click-Through Rate PredictionNeural Architecture SearchOne-Shot LearningTake Package as Language: Anomaly Detection Using Transformer
Network data packet anomaly detection faces numerous challenges, including exploring new anomaly supervision signals, researching weakly supervised anomaly detection, and improving model interpretability. This paper prop…
Anomaly DetectionIntrusion DetectionLanguage ModelingLanguage Modelling+5One-Shot Manipulation Strategy Learning by Making Contact Analogies
We present a novel approach, MAGIC (manipulation analogies for generalizable intelligent contacts), for one-shot learning of manipulation strategies with fast and extensive generalization to novel objects. By leveraging …
One-Shot LearningSupervised Learning without Backpropagation using Spike-Timing-Dependent Plasticity for Image Recognition
This study introduces a novel supervised learning approach for spiking neural networks that does not rely on traditional backpropagation. Instead, it employs spike-timing-dependent plasticity (STDP) within a supervised f…
One-Shot LearningRePD: Defending Jailbreak Attack through a Retrieval-based Prompt Decomposition Process
In this study, we introduce RePD, an innovative attack Retrieval-based Prompt Decomposition framework designed to mitigate the risk of jailbreak attacks on large language models (LLMs). Despite rigorous pretraining and f…
One-Shot LearningRetrievalSeqNet: Sequential Networks for One-Shot Traffic Sign Recognition With Transfer Learning
In traffic sign recognition tasks, recognition of road signs by observing synthetic reference images is a human-like ability that can be performed by one-shot learning algorithms. One-shot object recognition is a challen…
Image RetrievalMetric LearningObject RecognitionOne-Shot Learning+2LLMs are One-Shot URL Classifiers and Explainers
Malicious URL classification represents a crucial aspect of cyber security. Although existing work comprises numerous machine learning and deep learning-based URL classification models, most suffer from generalisation an…
Domain AdaptationInformativenessOne-Shot LearningDirect Data-Driven Discounted Infinite Horizon Linear Quadratic Regulator with Robustness Guarantees
This paper presents a one-shot learning approach with performance and robustness guarantees for the linear quadratic regulator (LQR) control of stochastic linear systems. Even though data-based LQR control has been widel…
Data Driven Optimal ControlOne-Shot LearningOne-Shot Learning for Pose-Guided Person Image Synthesis in the Wild
Current Pose-Guided Person Image Synthesis (PGPIS) methods depend heavily on large amounts of labeled triplet data to train the generator in a supervised manner. However, they often falter when applied to in-the-wild sam…
GPUImage GenerationOne-Shot LearningPose Transfer+1Abstracted Gaussian Prototypes for One-Shot Concept Learning
We introduce a cluster-based generative image segmentation framework to encode higher-level representations of visual concepts based on one-shot learning inspired by the Omniglot Challenge. The inferred parameters of eac…
Image SegmentationOne-Shot LearningSemantic SegmentationOne-Shot Learning Meets Depth Diffusion in Multi-Object Videos
Creating editable videos that depict complex interactions between multiple objects in various artistic styles has long been a challenging task in filmmaking. Progress is often hampered by the scarcity of data sets that c…
One-Shot LearningVideo GenerationEmulating Brain-like Rapid Learning in Neuromorphic Edge Computing
Achieving personalized intelligence at the edge with real-time learning capabilities holds enormous promise in enhancing our daily experiences and helping decision making, planning, and sensing. However, efficient and re…
Edge-computingOne-Shot LearningTransfer LearningCrossFi: A Cross Domain Wi-Fi Sensing Framework Based on Siamese Network
In recent years, Wi-Fi sensing has garnered significant attention due to its numerous benefits, such as privacy protection, low cost, and penetration ability. Extensive research has been conducted in this field, focusing…
Action ClassificationAction Classification (1-shot)Action Classification (zero-shot)Few-Shot Learning+6Correlation Weighted Prototype-based Self-Supervised One-Shot Segmentation of Medical Images
Medical image segmentation is one of the domains where sufficient annotated data is not available. This necessitates the application of low-data frameworks like few-shot learning. Contemporary prototype-based frameworks …
Few-Shot LearningImage SegmentationMedical Image SegmentationOne-Shot Learning+4MU-MAE: Multimodal Masked Autoencoders-Based One-Shot Learning
With the exponential growth of multimedia data, leveraging multimodal sensors presents a promising approach for improving accuracy in human activity recognition. Nevertheless, accurately identifying these activities usin…
Activity RecognitionHuman Activity RecognitionOne-Shot LearningOne-Shot Collaborative Data Distillation
Large machine-learning training datasets can be distilled into small collections of informative synthetic data samples. These synthetic sets support efficient model learning and reduce the communication cost of data shar…
One-Shot LearningEncoding Matching Criteria for Cross-domain Deformable Image Registration
Most existing deep learning-based registration methods are trained on single-type images to address same-domain tasks.However, cross-domain deformable registration remains challenging.We argue that the tailor-made matchi…
Image RegistrationOne-Shot Learning