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 9826–9850 of 10307 papers

TitleStatusHype
MoTE: Reconciling Generalization with Specialization for Visual-Language to Video Knowledge TransferCode0
Motion Planning NetworksCode0
Motion-related Artefact Classification Using Patch-based Ensemble and Transfer Learning in Cardiac MRICode0
Motley: Benchmarking Heterogeneity and Personalization in Federated LearningCode0
MR-based synthetic CT generation using a deep convolutional neural network methodCode0
MRCBert: A Machine Reading ComprehensionApproach for Unsupervised SummarizationCode0
MSciNLI: A Diverse Benchmark for Scientific Natural Language InferenceCode0
MSVD-Indonesian: A Benchmark for Multimodal Video-Text Tasks in IndonesianCode0
MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property PredictionCode0
MTG: A Benchmark Suite for Multilingual Text GenerationCode0
MuDPT: Multi-modal Deep-symphysis Prompt Tuning for Large Pre-trained Vision-Language ModelsCode0
MuLan: A Joint Embedding of Music Audio and Natural LanguageCode0
Multiband VAE: Latent Space Alignment for Knowledge Consolidation in Continual LearningCode0
Multicenter Privacy-Preserving Model Training for Deep Learning Brain Metastases AutosegmentationCode0
Multi-dataset and Transfer Learning Using Gene Expression Knowledge GraphsCode0
Multi-Domain Neural Machine Translation with Word-Level Adaptive Layer-wise Domain MixingCode0
Multi-fidelity Fourier Neural Operator for Fast Modeling of Large-Scale Geological Carbon StorageCode0
Multi-granular Legal Topic Classification on Greek LegislationCode0
Multi-Hop Fact Checking of Political ClaimsCode0
Multi-label classification for multi-temporal, multi-spatial coral reef condition monitoring using vision foundation model with adapter learningCode0
Multi-label Iterated Learning for Image Classification with Label AmbiguityCode0
Multi-Level and Multi-Scale Feature Aggregation Using Pre-trained Convolutional Neural Networks for Music Auto-taggingCode0
Multi-level Fusion of Wav2vec 2.0 and BERT for Multimodal Emotion RecognitionCode0
Multilinear Compressive Learning with Prior KnowledgeCode0
Multilingual Auxiliary Tasks Training: Bridging the Gap between Languages for Zero-Shot Transfer of Hate Speech Detection ModelsCode0
Show:102550
← PrevPage 394 of 413Next →
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