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 126–150 of 10307 papers

TitleStatusHype
Sky-image-based solar forecasting using deep learning with multi-location data: training models locally, globally or via transfer learning?Code2
Deep Model ReassemblyCode2
Content-Based Search for Deep Generative ModelsCode2
3D UX-Net: A Large Kernel Volumetric ConvNet Modernizing Hierarchical Transformer for Medical Image SegmentationCode2
On Efficient Reinforcement Learning for Full-length Game of StarCraft IICode2
Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series ClassificationCode2
Tip-Adapter: Training-free Adaption of CLIP for Few-shot ClassificationCode2
SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite ImageryCode2
Current Trends in Deep Learning for Earth Observation: An Open-source Benchmark Arena for Image ClassificationCode2
On-Device Training Under 256KB MemoryCode2
LST: Ladder Side-Tuning for Parameter and Memory Efficient Transfer LearningCode2
Neural Prompt SearchCode2
Actuarial Applications of Natural Language Processing Using Transformers: Case Studies for Using Text Features in an Actuarial ContextCode2
Unifying Voxel-based Representation with Transformer for 3D Object DetectionCode2
Overcoming Catastrophic Forgetting in Zero-Shot Cross-Lingual GenerationCode2
TransTab: Learning Transferable Tabular Transformers Across TablesCode2
Deep Learning-Enabled Semantic Communication Systems with Task-Unaware Transmitter and Dynamic DataCode2
K-LITE: Learning Transferable Visual Models with External KnowledgeCode2
Unified Contrastive Learning in Image-Text-Label SpaceCode2
GroupViT: Semantic Segmentation Emerges from Text SupervisionCode2
InPars: Data Augmentation for Information Retrieval using Large Language ModelsCode2
A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of VehiclesCode2
Multi-Representation Adaptation Network for Cross-domain Image ClassificationCode2
How Well Do Sparse Imagenet Models Transfer?Code2
ExT5: Towards Extreme Multi-Task Scaling for Transfer LearningCode2
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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