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 2451–2475 of 10307 papers

TitleStatusHype
Transfer Learning across Different Chemical Domains: Virtual Screening of Organic Materials with Deep Learning Models Pretrained on Small Molecule and Chemical Reaction Data—0
Calibration-free online test-time adaptation for electroencephalography motor imagery decodingCode1
Knowledge Transfer from Vision Foundation Models for Efficient Training of Small Task-specific ModelsCode1
Stochastic Vision Transformers with Wasserstein Distance-Aware Attention—0
Clinical Risk Prediction Using Language Models: Benefits And Considerations—0
Precipitation Nowcasting With Spatial And Temporal Transfer Learning Using Swin-UNETR—0
Hyperpolyglot LLMs: Cross-Lingual Interpretability in Token EmbeddingsCode0
Transfer Learning in Robotics: An Upcoming Breakthrough? A Review of Promises and Challenges—0
Skilful Precipitation Nowcasting Using NowcastNet—0
Latent Alignment with Deep Set EEG Decoders—0
Grounding Foundation Models through Federated Transfer Learning: A General Framework—0
LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPSCode2
Natural Language Processing Through Transfer Learning: A Case Study on Sentiment Analysis—0
FedAL: Black-Box Federated Knowledge Distillation Enabled by Adversarial Learning—0
Self-training solutions for the ICCV 2023 GeoNet ChallengeCode0
Empowering COVID-19 Detection: Optimizing Performance Through Fine-Tuned EfficientNet Deep Learning Architecture—0
MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training—0
Exo2EgoDVC: Dense Video Captioning of Egocentric Procedural Activities Using Web Instructional Videos—0
Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomy—0
Adinkra Symbol Recognition using Classical Machine Learning and Deep Learning—0
Towards Difficulty-Agnostic Efficient Transfer Learning for Vision-Language ModelsCode0
Side4Video: Spatial-Temporal Side Network for Memory-Efficient Image-to-Video Transfer LearningCode1
Towards Transfer Learning for Large-Scale Image Classification Using Annealing-based Quantum Boltzmann Machines—0
Transformer-QEC: Quantum Error Correction Code Decoding with Transferable Transformers—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