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 4076–4100 of 10307 papers

TitleStatusHype
Multi-Task Pseudo-Label Learning for Non-Intrusive Speech Quality Assessment Model—0
Time Series Predictions in Unmonitored Sites: A Survey of Machine Learning Techniques in Water Resources—0
Knowledge-inspired Subdomain Adaptation for Cross-Domain Knowledge Transfer—0
Multi-fidelity Fourier Neural Operator for Fast Modeling of Large-Scale Geological Carbon StorageCode0
S2R: Exploring a Double-Win Transformer-Based Framework for Ideal and Blind Super-ResolutionCode0
Sarcasm Detection in a Disaster ContextCode0
KMF: Knowledge-Aware Multi-Faceted Representation Learning for Zero-Shot Node Classification—0
Leveraging Codebook Knowledge with NLI and ChatGPT for Zero-Shot Political Relation ClassificationCode0
Interaction-Aware Personalized Vehicle Trajectory Prediction Using Temporal Graph Neural Networks—0
The Performance of Transferability Metrics does not Translate to Medical TasksCode0
Contrastive Bi-Projector for Unsupervised Domain AdaptionCode0
SEMI-CenterNet: A Machine Learning Facilitated Approach for Semiconductor Defect Inspection—0
Optimizing Brain Tumor Classification: A Comprehensive Study on Transfer Learning and Imbalance Handling in Deep Learning ModelsCode0
Tissue Segmentation of Thick-Slice Fetal Brain MR Scans with Guidance from High-Quality Isotropic Volumes—0
A Sequential Meta-Transfer (SMT) Learning to Combat Complexities of Physics-Informed Neural Networks: Application to Composites Autoclave ProcessingCode0
Smart Knowledge Transfer using Google-like Search—0
SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models—0
Fast and Accurate Transferability Measurement by Evaluating Intra-class Feature Variance—0
Classification of All Blood Cell Images using ML and DL Models—0
Monitoring of Urban Changes with multi-modal Sentinel 1 and 2 Data in Mariupol, Ukraine, in 2022/23Code0
Tweet Sentiment Extraction using Viterbi Algorithm with Transfer LearningCode0
CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigation—0
Addressing Data Scarcity in Optical Matrix Multiplier Modeling Using Transfer Learning—0
Deep Learning Model Transfer in Forest Mapping using Multi-source Satellite SAR and Optical Images—0
Weakly Semi-Supervised Detection in Lung Ultrasound Videos—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