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 476–500 of 10307 papers

TitleStatusHype
BIOSCAN-5M: A Multimodal Dataset for Insect BiodiversityCode1
BiToD: A Bilingual Multi-Domain Dataset For Task-Oriented Dialogue ModelingCode1
EfficientFER: EfficientNetv2 Based Deep Learning Approach for Facial Expression RecognitionCode1
Accuracy enhancement method for speech emotion recognition from spectrogram using temporal frequency correlation and positional information learning through knowledge transferCode1
Boosted Neural Decoders: Achieving Extreme Reliability of LDPC Codes for 6G NetworksCode1
Efficient Training of Large Vision Models via Advanced Automated Progressive LearningCode1
Efficient transfer learning for NLP with ELECTRACode1
Convolutional Bypasses Are Better Vision Transformer AdaptersCode1
Cross-Layer Distillation with Semantic CalibrationCode1
AdaBoost-CNN: An adaptive boosting algorithm for convolutional neural networks to classify multi-class imbalanced datasets using transfer learningCode1
Active, Continual Fine Tuning of Convolutional Neural Networks for Reducing Annotation EffortsCode1
Unlocking Emergent Modularity in Large Language ModelsCode1
A Further Study of Unsupervised Pre-training for Transformer Based Speech RecognitionCode1
A fuzzy distance-based ensemble of deep models for cervical cancer detectionCode1
Emotion Recognition from Speech Using Wav2vec 2.0 EmbeddingsCode1
Boosting Weakly Supervised Object Detection via Learning Bounding Box AdjustersCode1
Boosting Weakly Supervised Object Detection with Progressive Knowledge TransferCode1
Encapsulating Knowledge in One PromptCode1
End-to-end lyrics Recognition with Voice to Singing Style TransferCode1
Deepfake Videos in the Wild: Analysis and DetectionCode1
Enhancing Traffic Safety with Parallel Dense Video Captioning for End-to-End Event AnalysisCode1
BrainWave: A Brain Signal Foundation Model for Clinical ApplicationsCode1
Breaking the Data Barrier -- Building GUI Agents Through Task GeneralizationCode1
Omnidirectional Transfer for Quasilinear Lifelong LearningCode1
Position: Considerations for Differentially Private Learning with Large-Scale Public PretrainingCode1
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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