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 1876–1900 of 10307 papers

TitleStatusHype
Low-Resource Named Entity Recognition with Cross-Lingual, Character-Level Neural Conditional Random Fields—0
Breast Cancer Image Classification Method Based on Deep Transfer Learning—0
FedDistill: Global Model Distillation for Local Model De-Biasing in Non-IID Federated Learning—0
Intelligent Chemical Purification Technique Based on Machine Learning—0
Rethinking Low-Rank Adaptation in Vision: Exploring Head-Level Responsiveness across Diverse Tasks—0
Constructing and Exploring Intermediate Domains in Mixed Domain Semi-supervised Medical Image SegmentationCode2
E3: Ensemble of Expert Embedders for Adapting Synthetic Image Detectors to New Generators Using Limited DataCode0
Enhancing Traffic Safety with Parallel Dense Video Captioning for End-to-End Event AnalysisCode1
Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an example—0
Advanced wood species identification based on multiple anatomical sections and using deep feature transfer and fusion—0
Investigating Neural Machine Translation for Low-Resource Languages: Using Bavarian as a Case StudyCode0
Using Explainable AI and Transfer Learning to understand and predict the maintenance of Atlantic blocking with limited observational dataCode0
Transfer Learning Study of Motion Transformer-based Trajectory Predictions—0
MSciNLI: A Diverse Benchmark for Scientific Natural Language InferenceCode0
Predictive Handover Strategy in 6G and Beyond: A Deep and Transfer Learning Approach—0
GLID: Pre-training a Generalist Encoder-Decoder Vision Model—0
OpenTrench3D: A Photogrammetric 3D Point Cloud Dataset for Semantic Segmentation of Underground UtilitiesCode1
Depth Estimation using Weighted-loss and Transfer Learning—0
PINNACLE: PINN Adaptive ColLocation and Experimental points selectionCode1
Spatial Transfer Learning for Estimating PM2.5 in Data-poor RegionsCode0
We're Calling an Intervention: Exploring Fundamental Hurdles in Adapting Language Models to Nonstandard TextCode0
XNLIeu: a dataset for cross-lingual NLI in BasqueCode0
Adapting LLaMA Decoder to Vision TransformerCode1
The Sandwich meta-framework for architecture agnostic deep privacy-preserving transfer learning for non-invasive brainwave decoding—0
Using Few-Shot Learning to Classify Primary Lung Cancer and Other Malignancy with Lung Metastasis in Cytological Imaging via Endobronchial Ultrasound Procedures—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