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 7126–7150 of 10307 papers

TitleStatusHype
Supervised Transfer Learning for Product Information Question Answering—0
Supervised Transfer Learning Framework for Fault Diagnosis in Wind Turbines—0
Supervised Understanding of Word Embeddings—0
Supervising the Transfer of Reasoning Patterns in VQA—0
Support-BERT: Predicting Quality of Question-Answer Pairs in MSDN using Deep Bidirectional Transformer—0
Surface EMG-Based Inter-Session/Inter-Subject Gesture Recognition by Leveraging Lightweight All-ConvNet and Transfer Learning—0
Surface-Enhanced Raman Spectroscopy and Transfer Learning Toward Accurate Reconstruction of the Surgical Zone—0
Surf at MEDIQA 2019: Improving Performance of Natural Language Inference in the Clinical Domain by Adopting Pre-trained Language Model—0
Bristle: Decentralized Federated Learning in Byzantine, Non-i.i.d. Environments—0
SURFNet: Super-resolution of Turbulent Flows with Transfer Learning using Small Datasets—0
Surgical Phase Recognition of Short Video Shots Based on Temporal Modeling of Deep Features—0
SurgPETL: Parameter-Efficient Image-to-Surgical-Video Transfer Learning for Surgical Phase Recognition—0
Surprise Language Challenge: Developing a Neural Machine Translation System between Pashto and English in Two Months—0
Surrogate Empowered Sim2Real Transfer of Deep Reinforcement Learning for ORC Superheat Control—0
Surrogate Supervision for Medical Image Analysis: Effective Deep Learning From Limited Quantities of Labeled Data—0
Surveying You Only Look Once (YOLO) Multispectral Object Detection Advancements, Applications And Challenges—0
Survival and grade of the glioma prediction using transfer learning—0
BugWhisperer: Fine-Tuning LLMs for SoC Hardware Vulnerability Detection—0
Sustainable Smart Farm Networks: Enhancing Resilience and Efficiency with Decision Theory-Guided Deep Reinforcement Learning—0
SVD-PINNs: Transfer Learning of Physics-Informed Neural Networks via Singular Value Decomposition—0
Swapped Face Detection using Deep Learning and Subjective Assessment—0
Swing Distillation: A Privacy-Preserving Knowledge Distillation Framework—0
SWIPTNet: A Unified Deep Learning Framework for SWIPT based on GNN and Transfer Learning—0
Multihop: Leveraging Complex Models to Learn Accurate Simple Models—0
Swiss Army Knife: Synergizing Biases in Knowledge from Vision Foundation Models for Multi-Task Learning—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