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 2276–2300 of 10307 papers

TitleStatusHype
Transfer Learning of RSSI to Improve Indoor Localisation PerformanceCode0
A Novel Ensemble-Based Deep Learning Model with Explainable AI for Accurate Kidney Disease Diagnosis—0
BayesAdapter: enhanced uncertainty estimation in CLIP few-shot adaptation—0
A Wander Through the Multimodal Landscape: Efficient Transfer Learning via Low-rank Sequence Multimodal AdapterCode0
Text Generation Models for Luxembourgish with Limited Data: A Balanced Multilingual Strategy—0
Evaluating Pixel Language Models on Non-Standardized Languages—0
Improvement in Sign Language Translation Using Text CTC AlignmentCode0
Unified HT-CNNs Architecture: Transfer Learning for Segmenting Diverse Brain Tumors in MRI from Gliomas to Pediatric Tumors—0
Beyond Knowledge Silos: Task Fingerprinting for Democratization of Medical Imaging AICode0
Unsupervised Cross-Domain Regression for Fine-grained 3D Game Character Reconstruction—0
Improving Satellite Imagery Masking using Multi-task and Transfer Learning—0
ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts—0
Efficient Gravitational Wave Parameter Estimation via Knowledge Distillation: A ResNet1D-IAF Approach—0
Enhancing Drug-Target Interaction Prediction through Transfer Learning from Activity Cliff Prediction TasksCode0
Enhancing radioisotope identification in gamma spectra via supervised domain adaptation—0
Annotation Techniques for Judo Combat Phase Classification from Tournament Footage—0
PETALface: Parameter Efficient Transfer Learning for Low-resolution Face Recognition—0
An Enhancement of CNN Algorithm for Rice Leaf Disease Image Classification in Mobile Applications—0
Real-time Sign Language Recognition Using MobileNetV2 and Transfer Learning—0
Dynamic Ensemble Reasoning for LLM Experts—0
SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation—0
Harnessing Transfer Learning from Swahili: Advancing Solutions for Comorian Dialects—0
How transfer learning is used in generative models for image classification: improved accuracyCode0
Representational Transfer Learning for Matrix Completion—0
Self-Supervised Learning with Probabilistic Density Labeling for Rainfall Probability EstimationCode0
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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