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 676–700 of 10307 papers

TitleStatusHype
Domain Generalization for Prostate Segmentation in Transrectal Ultrasound Images: A Multi-center StudyCode1
Transfer Learning of an Ensemble of DNNs for SSVEP BCI Spellers without User-Specific TrainingCode1
A New Knowledge Distillation Network for Incremental Few-Shot Surface Defect DetectionCode1
Enabling Country-Scale Land Cover Mapping with Meter-Resolution Satellite ImageryCode1
ViA: View-invariant Skeleton Action Representation Learning via Motion RetargetingCode1
Progressive Self-Distillation for Ground-to-Aerial Perception Knowledge TransferCode1
Grounded Affordance from Exocentric ViewCode1
PANDA: Prompt Transfer Meets Knowledge Distillation for Efficient Model AdaptationCode1
DenseShift: Towards Accurate and Efficient Low-Bit Power-of-Two QuantizationCode1
Conv-Adapter: Exploring Parameter Efficient Transfer Learning for ConvNetsCode1
GPPT: Graph Pre-training and Prompt Tuning to Generalize Graph Neural NetworksCode1
MixSKD: Self-Knowledge Distillation from Mixup for Image RecognitionCode1
Differencing based Self-supervised pretraining for Scene Change DetectionCode1
Probabilistic forecasts of extreme heatwaves using convolutional neural networks in a regime of lack of dataCode1
CrAM: A Compression-Aware MinimizerCode1
W2N:Switching From Weak Supervision to Noisy Supervision for Object DetectionCode1
Self-supervised contrastive learning of echocardiogram videos enables label-efficient cardiac disease diagnosisCode1
Online Knowledge Distillation via Mutual Contrastive Learning for Visual RecognitionCode1
Hyper-Representations for Pre-Training and Transfer LearningCode1
Unsupervised pre-training of graph transformers on patient population graphsCode1
Transfer Learning of wav2vec 2.0 for Automatic Lyric TranscriptionCode1
GenHPF: General Healthcare Predictive Framework with Multi-task Multi-source LearningCode1
Simplified Transfer Learning for Chest Radiography Models Using Less DataCode1
Online Dynamics Learning for Predictive Control with an Application to Aerial RobotsCode1
Learning with Recoverable ForgettingCode1
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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