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 3651–3675 of 10307 papers

TitleStatusHype
Teamwork Dimensions Classification Using BERT—0
Model Evaluation for Domain Identification of Unknown Classes in Open-World Recognition: A Proposal—0
Data Scarcity in Recommendation Systems: A Survey—0
Small Area Estimation of Case Growths for Timely COVID-19 Outbreak DetectionCode0
GYM at Qur’an QA 2023 Shared Task: Multi-Task Transfer Learning for Quranic Passage Retrieval and Question Answering with Large Language ModelsCode0
Enhancing Polynomial Chaos Expansion Based Surrogate Modeling using a Novel Probabilistic Transfer Learning Strategy—0
TLCE: Transfer-Learning Based Classifier Ensembles for Few-Shot Class-Incremental Learning—0
Optimizing Two-Pass Cross-Lingual Transfer Learning: Phoneme Recognition and Phoneme to Grapheme Translation—0
Decoding Working-Memory Load During n-Back Task Performance from High Channel NIRS Data—0
Customizable Combination of Parameter-Efficient Modules for Multi-Task Learning—0
Enhanced Breast Cancer Tumor Classification using MobileNetV2: A Detailed Exploration on Image Intensity, Error Mitigation, and Streamlit-driven Real-time Deployment—0
Similarity-based Knowledge Transfer for Cross-Domain Reinforcement Learning—0
Hot PATE: Private Aggregation of Distributions for Diverse Task—0
Facial Emotion Recognition Under Mask Coverage Using a Data Augmentation TechniqueCode0
Robust Computer Vision in an Ever-Changing World: A Survey of Techniques for Tackling Distribution Shifts—0
A Survey on Stability of Learning with Limited Labelled Data and its Sensitivity to the Effects of Randomness—0
Code-Mixed Text to Speech Synthesis under Low-Resource Constraints—0
Efficient Expansion and Gradient Based Task Inference for Replay Free Incremental Learning—0
SASSL: Enhancing Self-Supervised Learning via Neural Style Transfer—0
Acoustic Signal Analysis with Deep Neural Network for Detecting Fault Diagnosis in Industrial Machines—0
Disentangling the Effects of Data Augmentation and Format Transform in Self-Supervised Learning of Image Representations—0
Rapid Speaker Adaptation in Low Resource Text to Speech Systems using Synthetic Data and Transfer learning—0
A Comparative Analysis Towards Melanoma Classification Using Transfer Learning by Analyzing Dermoscopic Images—0
Student Activity Recognition in Classroom Environments using Transfer Learning—0
Pathway to a fully data-driven geotechnics: lessons from materials informatics—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