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 1376–1400 of 10307 papers

TitleStatusHype
Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine TranslationCode1
DenseShift: Towards Accurate and Efficient Low-Bit Power-of-Two QuantizationCode1
DEPARA: Deep Attribution Graph for Deep Knowledge TransferabilityCode1
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language UnderstandingCode1
GOAL: A Generalist Combinatorial Optimization Agent LearningCode1
Anatomical Foundation Models for Brain MRIsCode1
Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotationsCode1
Golos: Russian Dataset for Speech ResearchCode1
Graph Contrastive Learning with AugmentationsCode1
Open-Vocabulary Multi-Label Classification via Multi-Modal Knowledge TransferCode1
ASSET: Robust Backdoor Data Detection Across a Multiplicity of Deep Learning ParadigmsCode1
A Comprehensive Approach for UAV Small Object Detection with Simulation-based Transfer Learning and Adaptive FusionCode1
BARThez: a Skilled Pretrained French Sequence-to-Sequence ModelCode1
Overcoming Data Limitations: A Few-Shot Specific Emitter Identification Method Using Self-Supervised Learning and Adversarial AugmentationCode1
A Survey on Negative TransferCode1
Detection and Classification of Diabetic Retinopathy using Deep Learning Algorithms for Segmentation to Facilitate Referral Recommendation for Test and Treatment PredictionCode1
PACTran: PAC-Bayesian Metrics for Estimating the Transferability of Pretrained Models to Classification TasksCode1
PALT: Parameter-Lite Transfer of Language Models for Knowledge Graph CompletionCode1
Enhanced Gaussian Process Dynamical Models with Knowledge Transfer for Long-term Battery Degradation ForecastingCode1
Benchmarking Detection Transfer Learning with Vision TransformersCode1
Determining Chess Game State From an ImageCode1
Developing Personalized Models of Blood Pressure Estimation from Wearable Sensors Data Using Minimally-trained Domain Adversarial Neural NetworksCode1
Development and bilingual evaluation of Japanese medical large language model within reasonably low computational resourcesCode1
Reasoning Visual Dialog with Sparse Graph Learning and Knowledge TransferCode1
Geometric Knowledge Distillation: Topology Compression for Graph Neural NetworksCode1
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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