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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 8376–8400 of 10307 papers

TitleStatusHype
Transfer Learning via Minimizing the Performance Gap Between DomainsCode0
X4D-SceneFormer: Enhanced Scene Understanding on 4D Point Cloud Videos through Cross-modal Knowledge TransferCode0
Transfer Learning for Automated Test Case Prioritization Using XCSFCode0
Transfer Learning via Unsupervised Task Discovery for Visual Question AnsweringCode0
Universal Language Model Fine-Tuning with Subword Tokenization for PolishCode0
Transfer learning with affine model transformationCode0
Weakly and Semi Supervised Human Body Part Parsing via Pose-Guided Knowledge TransferCode0
TRANSFER :- DEEP INDUCTIVE NETWORK FOR FACIAL EMOTION RECOGNITIONCode0
Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised ApproachCode0
TokenVerse: Towards Unifying Speech and NLP Tasks via Transducer-based ASRCode0
Towards Offensive Language Identification for Dravidian LanguagesCode0
Transfer Learning with Convolutional Neural Networks for Rainfall Detection in Single ImagesCode0
Weakly-supervised Deep Cognate Detection Framework for Low-Resourced Languages Using Morphological Knowledge of Closely-Related LanguagesCode0
Transfer Learning for Activity Recognition in Mobile HealthCode0
Transfer Learning Using Ensemble Neural Networks for Organic Solar Cell ScreeningCode0
TMI! Finetuned Models Leak Private Information from their Pretraining DataCode0
When to Use Multi-Task Learning vs Intermediate Fine-Tuning for Pre-Trained Encoder Transfer LearningCode0
Weakly Supervised Knowledge Transfer with Probabilistic Logical Reasoning for Object DetectionCode0
Zero-shot Knowledge Transfer via Adversarial Belief MatchingCode0
Transfer Learning Enhanced Generative Adversarial Networks for Multi-Channel MRI ReconstructionCode0
When Vision Transformers Outperform ResNets without Pre-training or Strong Data AugmentationsCode0
Transfer Learning with Gaussian Processes for Bayesian OptimizationCode0
Transfer Capsule Network for Aspect Level Sentiment ClassificationCode0
Transfer learning using deep neural networks for Ear Presentation Attack Detection: New Database for PADCode0
Toward Extremely Lightweight Distracted Driver Recognition With Distillation-Based Neural Architecture Search and Knowledge TransferCode0
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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