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 7126–7150 of 10307 papers

TitleStatusHype
Transfer or Self-Supervised? Bridging the Performance Gap in Medical Imaging—0
The Power of Transfer Learning in Agricultural Applications: AgriNet—0
Less is More: Undertraining Experts Improves Model Upcycling—0
LESS: Large Language Model Enhanced Semi-Supervised Learning for Speech Foundational Models—0
Lesson Learnt: Modularization of Deep Networks Allow Cross-Modality Reuse—0
Simultaneously Evolving Deep Reinforcement Learning Models using Multifactorial Optimization—0
Lessons from the Use of Natural Language Inference (NLI) in Requirements Engineering Tasks—0
A new semi-supervised inductive transfer learning framework: Co-Transfer—0
Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification—0
Transfer Learning of Real Image Features with Soft Contrastive Loss for Fake Image Detection—0
Let's Focus: Focused Backdoor Attack against Federated Transfer Learning—0
Letter Sequence Labeling for Compound Splitting—0
A new Potential-Based Reward Shaping for Reinforcement Learning Agent—0
SINAI at SemEval-2020 Task 12: Offensive Language Identification Exploring Transfer Learning Models—0
A New Perspective on Smiling and Laughter Detection: Intensity Levels Matter—0
Leveraging Affect Transfer Learning for Behavior Prediction in an Intelligent Tutoring System—0
SINAI-DL at SemEval-2019 Task 7: Data Augmentation and Temporal Expressions—0
Leveraging ASR Pretrained Conformers for Speaker Verification through Transfer Learning and Knowledge Distillation—0
Leveraging Auxiliary Task Relevance for Enhanced Bearing Fault Diagnosis through Curriculum Meta-learning—0
Leveraging Cross-Attention Transformer and Multi-Feature Fusion for Cross-Linguistic Speech Emotion Recognition—0
Leveraging Distillation Techniques for Document Understanding: A Case Study with FLAN-T5—0
Leveraging Foundation Models for Multi-modal Federated Learning with Incomplete Modality—0
Leveraging Industry 4.0 -- Deep Learning, Surrogate Model and Transfer Learning with Uncertainty Quantification Incorporated into Digital Twin for Nuclear System—0
Model-Based and Data-Driven Strategies in Medical Image Computing—0
Leveraging Local Domains for Image-to-Image Translation—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