SOTAVerified

Transfer Learning

Transfer Learning is a machine learning technique where a model trained on one task is re-purposed and fine-tuned for a related, but different task. The idea behind transfer learning is to leverage the knowledge learned from a pre-trained model to solve a new, but related problem. This can be useful in situations where there is limited data available to train a new model from scratch, or when the new task is similar enough to the original task that the pre-trained model can be adapted to the new problem with only minor modifications.

( Image credit: Subodh Malgonde )

Papers

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Showing 9551–9600 of 10307 papers

TitleStatusHype
Dynamic Flows on Curved Space Generated by Labeled Data—0
Dynamic Gazetteer Integration in Multilingual Models for Cross-Lingual and Cross-Domain Named Entity Recognition—0
Dynamic Indoor Fingerprinting Localization based on Few-Shot Meta-Learning with CSI Images—0
Dynamic Knowledge Distillation for Black-box Hypothesis Transfer Learning—0
Dynamic Schema Graph Fusion Network for Multi-Domain Dialogue State Tracking—0
Dynamic Schema Graph Fusion Network for Multi-Domain Dialogue State Tracking—0
Dynamic Transfer Learning for Named Entity Recognition—0
Dynamic Visual Prompt Tuning for Parameter Efficient Transfer Learning—0
DynaQuant: Compressing Deep Learning Training Checkpoints via Dynamic Quantization—0
Early detection of diabetes through transfer learning-based eye (vision) screening and improvement of machine learning model performance and advanced parameter setting algorithms—0
Early Diagnosis and Severity Assessment of Weligama Coconut Leaf Wilt Disease and Coconut Caterpillar Infestation using Deep Learning-based Image Processing Techniques—0
Early Diagnosis of Chronic Obstructive Pulmonary Disease from Chest X-Rays using Transfer Learning and Fusion Strategies—0
Early Diagnosis of Parkinsons Disease by Analyzing Magnetic Resonance Imaging Brain Scans and Patient Characteristics—0
Early Prediction of Sepsis: Feature-Aligned Transfer Learning—0
Early-Stopping for Meta-Learning: Estimating Generalization from the Activation Dynamics—0
Easy Transfer Learning By Exploiting Intra-domain Structures—0
ECAT: A Entire space Continual and Adaptive Transfer Learning Framework for Cross-Domain Recommendation—0
ECAvg: An Edge-Cloud Collaborative Learning Approach using Averaged Weights—0
ECG Arrhythmia Classification Using Transfer Learning from 2-Dimensional Deep CNN Features—0
ECG-CL: A Comprehensive Electrocardiogram Interpretation Method Based on Continual Learning—0
ECG Heartbeat classification using deep transfer learning with Convolutional Neural Network and STFT technique—0
EchoLM: Accelerating LLM Serving with Real-time Knowledge Distillation—0
ECLeKTic: a Novel Challenge Set for Evaluation of Cross-Lingual Knowledge Transfer—0
EventBind: Learning a Unified Representation to Bind Them All for Event-based Open-world Understanding—0
Attention-Enhanced Prioritized Proximal Policy Optimization for Adaptive Edge Caching—0
Edge Caching Optimization with PPO and Transfer Learning for Dynamic Environments—0
Edge-cloud Collaborative Learning with Federated and Centralized Features—0
EdgeFD: An Edge-Friendly Drift-Aware Fault Diagnosis System for Industrial IoT—0
Edinburgh’s End-to-End Multilingual Speech Translation System for IWSLT 2021—0
EDIOne@LT-EDI-EACL2021: Pre-trained Transformers with Convolutional Neural Networks for Hope Speech Detection.—0
Edit Once, Update Everywhere: A Simple Framework for Cross-Lingual Knowledge Synchronization in LLMs—0
Edit Transfer: Learning Image Editing via Vision In-Context Relations—0
EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and their Applications—0
EEG-based Classification of Drivers Attention using Convolutional Neural Network—0
EEG-based Cognitive Load Classification using Feature Masked Autoencoding and Emotion Transfer Learning—0
EEG-Based Mental Imagery Task Adaptation via Ensemble of Weight-Decomposed Low-Rank Adapters—0
EEG Decoding for Datasets with Heterogenous Electrode Configurations using Transfer Learning Graph Neural Networks—0
EEG-NeXt: A Modernized ConvNet for The Classification of Cognitive Activity from EEG—0
EEGPT: Unleashing the Potential of EEG Generalist Foundation Model by Autoregressive Pre-training—0
Effective Analog ICs Floorplanning with Relational Graph Neural Networks and Reinforcement Learning—0
Effective and Efficient Cross-City Traffic Knowledge Transfer: A Privacy-Preserving Perspective—0
Effective Domain Knowledge Transfer with Soft Fine-tuning—0
Effective Few-Shot Classification with Transfer Learning—0
Effectiveness of Arbitrary Transfer Sets for Data-free Knowledge Distillation—0
Effectiveness of Mining Audio and Text Pairs from Public Data for Improving ASR Systems for Low-Resource Languages—0
Effective Representations of Clinical Notes—0
Effective training of deep convolutional neural networks for hyperspectral image classification through artificial labeling—0
Effective Transfer Learning for Identifying Similar Questions: Matching User Questions to COVID-19 FAQs—0
Effective Transfer Learning for Low-Resource Natural Language Understanding—0
Effective Two-Stage Knowledge Transfer for Multi-Entity Cross-Domain Recommendation—0
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All datasetsOffice-Home100 sleep nights of 8 caregiversBanglaLekha Isolated DatasetCOCO70KITTI Object Tracking Evaluation 2012Retinal Fundus MultiDisease Image Dataset (RFMiD)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1APCLIPAccuracy84.2—Unverified
2DFA-ENTAccuracy69.2—Unverified
3DFA-SAFNAccuracy69.1—Unverified
4EasyTLAccuracy63.3—Unverified
5MEDAAccuracy60.3—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN10-20% Mask PSNR3.23—Unverified
#ModelMetricClaimedVerifiedStatus
1Chatterjee, Dutta et al.[1]Accuracy96.12—Unverified
#ModelMetricClaimedVerifiedStatus
1Co-TuningAccuracy85.65—Unverified
#ModelMetricClaimedVerifiedStatus
1Physical AccessEER5.74—Unverified
#ModelMetricClaimedVerifiedStatus
1riadd.aucmediAUROC0.95—Unverified