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 3726–3750 of 10307 papers

TitleStatusHype
Automatic Sleep Stage Classification with Cross-modal Self-supervised Features from Deep Brain Signals—0
Transfer learning for process design with reinforcement learning—0
Adaptive Aggregation for Safety-Critical Control—0
Continual Pre-training of Language ModelsCode2
ClueGAIN: Application of Transfer Learning On Generative Adversarial Imputation Nets (GAIN)—0
Domain Adaptation for Time Series Under Feature and Label ShiftsCode1
Novel Fundus Image Preprocessing for Retcam Images to Improve Deep Learning Classification of Retinopathy of Prematurity—0
COVID-19 Infection Analysis Framework using Novel Boosted CNNs and Radiological Images—0
TAP: The Attention Patch for Cross-Modal Knowledge Transfer from Unlabeled Modality—0
MOMA:Distill from Self-Supervised Teachers—0
Self-Supervised In-Domain Representation Learning for Remote Sensing Image Scene Classification—0
Efficient Domain Adaptation for Speech Foundation Models—0
Interpretations of Domain Adaptations via Layer Variational AnalysisCode0
SoK: A Systematic Evaluation of Backdoor Trigger Characteristics in Image Classification—0
Over-parameterised Shallow Neural Networks with Asymmetrical Node Scaling: Global Convergence Guarantees and Feature LearningCode0
Paced-Curriculum Distillation with Prediction and Label Uncertainty for Image SegmentationCode1
An Empirical Study on the Transferability of Transformer Modules in Parameter-Efficient Fine-Tuning—0
Local transfer learning from one data space to another—0
Learning Universal Policies via Text-Guided Video Generation—0
Dynamic Flows on Curved Space Generated by Labeled Data—0
Supporting Safety Analysis of Image-processing DNNs through Clustering-based Approaches—0
Mind the (optimality) Gap: A Gap-Aware Learning Rate Scheduler for Adversarial Nets—0
Towards interpretable quantum machine learning via single-photon quantum walks—0
Transfer Learning and Class Decomposition for Detecting the Cognitive Decline of Alzheimer Disease—0
Knowledge Distillation Label Smoothing: Fact or Fallacy?—0
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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