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 5601–5625 of 10307 papers

TitleStatusHype
Beyond Fine-tuning: Few-Sample Sentence Embedding Transfer—0
Simple yet Effective Code-Switching Language Identification with Multitask Pre-Training and Transfer Learning—0
Sim-to-Real Optimization of Complex Real World Mobile Network with Imperfect Information via Deep Reinforcement Learning from Self-play—0
Sim-to-Real Transfer in Multi-agent Reinforcement Networking for Federated Edge Computing—0
Sim-to-Real Transfer Learning using Robustified Controllers in Robotic Tasks involving Complex Dynamics—0
Simultaneously Evolving Deep Reinforcement Learning Models using Multifactorial Optimization—0
SINAI at SemEval-2020 Task 12: Offensive Language Identification Exploring Transfer Learning Models—0
SINAI-DL at SemEval-2019 Task 7: Data Augmentation and Temporal Expressions—0
SingIt! Singer Voice Transformation—0
Single Image Action Recognition by Predicting Space-Time Saliency—0
Size doesn't matter: predicting physico- or biochemical properties based on dozens of molecules—0
Size Independent Neural Transfer for RDDL Planning—0
SkeleTR: Towards Skeleton-based Action Recognition in the Wild—0
SkeleTR: Towrads Skeleton-based Action Recognition in the Wild—0
Sketch-a-Classifier: Sketch-based Photo Classifier Generation—0
Skilful Precipitation Nowcasting Using NowcastNet—0
Skill Transfer in Deep Reinforcement Learning under Morphological Heterogeneity—0
Skin Cancer Classification using Inception Network and Transfer Learning—0
Skin Cancer Images Classification using Transfer Learning Techniques—0
Skin cancer reorganization and classification with deep neural network—0
Skin Disease Detection and Classification of Actinic Keratosis and Psoriasis Utilizing Deep Transfer Learning—0
Skin Lesion Analyser: An Efficient Seven-Way Multi-Class Skin Cancer Classification Using MobileNet—0
SKoPe3D: A Synthetic Dataset for Vehicle Keypoint Perception in 3D from Traffic Monitoring Cameras—0
SLABERT Talk Pretty One Day: Modeling Second Language Acquisition with BERT—0
Slash or burn: Power line and vegetation classification for wildfire prevention—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