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 9551–9575 of 10307 papers

TitleStatusHype
IIITT@DravidianLangTech-EACL2021: Transfer Learning for Offensive Language Detection in Dravidian LanguagesCode0
Image-based eeg classification of brain responses to song recordingsCode0
ImageNot: A contrast with ImageNet preserves model rankingsCode0
Image Understands Point Cloud: Weakly Supervised 3D Semantic Segmentation via Association LearningCode0
Imitation Learning for Generalizable Self-driving Policy with Sim-to-real TransferCode0
Implicit Cross-Lingual Rewarding for Efficient Multilingual Preference AlignmentCode0
Improved Multilingual Language Model Pretraining for Social Media Text via Translation Pair PredictionCode0
Improved Training for 3D Point Cloud ClassificationCode0
Improved transferability of self-supervised learning models through batch normalization finetuningCode0
Improvement in Sign Language Translation Using Text CTC AlignmentCode0
Improving 3D Medical Image Segmentation at Boundary Regions using Local Self-attention and Global Volume MixingCode0
Towards Difficulty-Agnostic Efficient Transfer Learning for Vision-Language ModelsCode0
Improving Automatic Jazz Melody Generation by Transfer Learning TechniquesCode0
Improving Automatic Speech Recognition for Non-Native English with Transfer Learning and Language Model DecodingCode0
Improving Calibration and Out-of-Distribution Detection in Medical Image Segmentation with Convolutional Neural NetworksCode0
Improving Compound Activity Classification via Deep Transfer and Representation LearningCode0
Improving Dialectal Slot and Intent Detection with Auxiliary Tasks: A Multi-Dialectal Bavarian Case StudyCode0
Improving Document Binarization via Adversarial Noise-Texture AugmentationCode0
FUN with Fisher: Improving Generalization of Adapter-Based Cross-lingual Transfer with Scheduled UnfreezingCode0
Improving Localization for Semi-Supervised Object DetectionCode0
Improving Meta-Learning Generalization with Activation-Based Early-StoppingCode0
Improving Representational Continuity via Continued PretrainingCode0
Improving Response Time of Home IoT Services in Federated LearningCode0
Improving Self-Supervised Learning-based MOS Prediction NetworksCode0
Improving significance of binary black hole mergers in Advanced LIGO data using deep learning : Confirmation of GW151216Code0
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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