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 8501–8525 of 10307 papers

TitleStatusHype
Transformer Training Strategies for Forecasting Multiple Load Time SeriesCode0
Towards Learning a Universal Non-Semantic Representation of SpeechCode0
Unsupervised Task Clustering for Multi-Task Reinforcement LearningCode0
Transfer Learning Across Heterogeneous Features For Efficient Tensor Program GenerationCode0
Unsupervised Transfer Learning for Spoken Language Understanding in Intelligent AgentsCode0
What Are We Measuring When We Evaluate Large Vision-Language Models? An Analysis of Latent Factors and BiasesCode0
Transfer Learning from Synthetic to Real-Noise Denoising with Adaptive Instance NormalizationCode0
TransFusion: Covariate-Shift Robust Transfer Learning for High-Dimensional RegressionCode0
Transfusion: Understanding Transfer Learning for Medical ImagingCode0
Winning the Lottery with Continuous SparsificationCode0
Transition-Aware Multi-Activity Knowledge TracingCode0
Discriminative Joint Probability Maximum Mean Discrepancy (DJP-MMD) for Domain AdaptationCode0
Towards Alzheimer's Disease Classification through Transfer LearningCode0
Translate and Classify: Improving Sequence Level Classification for English-Hindi Code-Mixed DataCode0
Translation Artifacts in Cross-lingual Transfer LearningCode0
Unsupervised Visual Representation Learning via Mutual Information Regularized AssignmentCode0
Unsupervised vs. transfer learning for multimodal one-shot matching of speech and imagesCode0
What does it take to bake a cake? The RecipeRef corpus and anaphora resolution in procedural textCode0
Transferability Metrics for Object DetectionCode0
Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech SynthesisCode0
Transfer learning from language models to image caption generators: Better models may not transfer betterCode0
Vision Transformers for Small Histological Datasets Learned through Knowledge DistillationCode0
Transductive conformal inference with adaptive scoresCode0
Transfer Learning from Deep Features for Remote Sensing and Poverty MappingCode0
Transfer-Learning Across Datasets with Different Input Dimensions: An Algorithm and Analysis for the Linear Regression CaseCode0
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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