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 3676–3700 of 10307 papers

TitleStatusHype
Explainable AI in Diagnosing and Anticipating Leukemia Using Transfer Learning Method—0
Pathway to a fully data-driven geotechnics: lessons from materials informatics—0
Simple Transferability Estimation for Regression TasksCode0
Stochastic Vision Transformers with Wasserstein Distance-Aware Attention—0
Enhancing Cross-domain Click-Through Rate Prediction via Explicit Feature Augmentation—0
Transfer Learning across Different Chemical Domains: Virtual Screening of Organic Materials with Deep Learning Models Pretrained on Small Molecule and Chemical Reaction Data—0
Learning Robust Precipitation Forecaster by Temporal Frame InterpolationCode0
Clinical Risk Prediction Using Language Models: Benefits And Considerations—0
Hyperpolyglot LLMs: Cross-Lingual Interpretability in Token EmbeddingsCode0
Transfer Learning in Robotics: An Upcoming Breakthrough? A Review of Promises and Challenges—0
Latent Alignment with Deep Set EEG Decoders—0
Skilful Precipitation Nowcasting Using NowcastNet—0
Precipitation Nowcasting With Spatial And Temporal Transfer Learning Using Swin-UNETR—0
Grounding Foundation Models through Federated Transfer Learning: A General Framework—0
Self-training solutions for the ICCV 2023 GeoNet ChallengeCode0
Natural Language Processing Through Transfer Learning: A Case Study on Sentiment Analysis—0
Empowering COVID-19 Detection: Optimizing Performance Through Fine-Tuned EfficientNet Deep Learning Architecture—0
Exo2EgoDVC: Dense Video Captioning of Egocentric Procedural Activities Using Web Instructional Videos—0
FedAL: Black-Box Federated Knowledge Distillation Enabled by Adversarial Learning—0
MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training—0
Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomy—0
Towards Difficulty-Agnostic Efficient Transfer Learning for Vision-Language ModelsCode0
Transformer-QEC: Quantum Error Correction Code Decoding with Transferable Transformers—0
Adinkra Symbol Recognition using Classical Machine Learning and Deep Learning—0
Machine Learning-Based Jamun Leaf Disease Detection: A Comprehensive Review—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