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 2426–2450 of 10307 papers

TitleStatusHype
Optimizing Two-Pass Cross-Lingual Transfer Learning: Phoneme Recognition and Phoneme to Grapheme Translation—0
Customizable Combination of Parameter-Efficient Modules for Multi-Task Learning—0
Parameter-Efficient Transfer Learning of Audio Spectrogram TransformersCode1
Does Vector Quantization Fail in Spatio-Temporal Forecasting? Exploring a Differentiable Sparse Soft-Vector Quantization ApproachCode1
Similarity-based Knowledge Transfer for Cross-Domain Reinforcement Learning—0
Enhanced Breast Cancer Tumor Classification using MobileNetV2: A Detailed Exploration on Image Intensity, Error Mitigation, and Streamlit-driven Real-time Deployment—0
Strong but simple: A Baseline for Domain Generalized Dense Perception by CLIP-based Transfer LearningCode1
Hot PATE: Private Aggregation of Distributions for Diverse Task—0
Robust Computer Vision in an Ever-Changing World: A Survey of Techniques for Tackling Distribution Shifts—0
Facial Emotion Recognition Under Mask Coverage Using a Data Augmentation TechniqueCode0
Code-Mixed Text to Speech Synthesis under Low-Resource Constraints—0
Rapid Speaker Adaptation in Low Resource Text to Speech Systems using Synthetic Data and Transfer learning—0
SASSL: Enhancing Self-Supervised Learning via Neural Style Transfer—0
Disentangling the Effects of Data Augmentation and Format Transform in Self-Supervised Learning of Image Representations—0
Efficient Expansion and Gradient Based Task Inference for Replay Free Incremental Learning—0
A Comparative Analysis Towards Melanoma Classification Using Transfer Learning by Analyzing Dermoscopic Images—0
Acoustic Signal Analysis with Deep Neural Network for Detecting Fault Diagnosis in Industrial Machines—0
A Survey on Stability of Learning with Limited Labelled Data and its Sensitivity to the Effects of Randomness—0
Transfer learning for predicting source terms of principal component transport in chemically reactive flow—0
Pathway to a fully data-driven geotechnics: lessons from materials informatics—0
Student Activity Recognition in Classroom Environments using Transfer Learning—0
Explainable AI in Diagnosing and Anticipating Leukemia Using Transfer Learning Method—0
Simple Transferability Estimation for Regression TasksCode0
Enhancing Cross-domain Click-Through Rate Prediction via Explicit Feature Augmentation—0
Learning Robust Precipitation Forecaster by Temporal Frame InterpolationCode0
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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