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 4001–4025 of 10307 papers

TitleStatusHype
Using LLMs to Establish Implicit User Sentiment of Software Desirability—0
FedLED: Label-Free Equipment Fault Diagnosis with Vertical Federated Transfer Learning—0
Decomposition-Based Transfer Distance Metric Learning for Image Classification—0
A Multi-media Approach to Cross-lingual Entity Knowledge Transfer—0
FedMEKT: Distillation-based Embedding Knowledge Transfer for Multimodal Federated Learning—0
FedMetaMed: Federated Meta-Learning for Personalized Medication in Distributed Healthcare Systems—0
Decomposed Cross-modal Distillation for RGB-based Temporal Action Detection—0
FedMobile: Enabling Knowledge Contribution-aware Multi-modal Federated Learning with Incomplete Modalities—0
Decomposable Probability-of-Success Metrics in Algorithmic Search—0
FedOpenHAR: Federated Multi-Task Transfer Learning for Sensor-Based Human Activity Recognition—0
Automated Segmentation and Analysis of Microscopy Images of Laser Powder Bed Fusion Melt Tracks—0
FedProK: Trustworthy Federated Class-Incremental Learning via Prototypical Feature Knowledge Transfer—0
Computer-aided Diagnosis of Malaria through Transfer Learning using the ResNet50 Backbone—0
FedSKD: Aggregation-free Model-heterogeneous Federated Learning using Multi-dimensional Similarity Knowledge Distillation—0
Adaptive Transfer Learning for Plant Phenotyping—0
GAMA++: Disentangled Geometric Alignment with Adaptive Contrastive Perturbation for Reliable Domain Transfer—0
FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers—0
FedZKT: Zero-Shot Knowledge Transfer towards Resource-Constrained Federated Learning with Heterogeneous On-Device Models—0
Gastrointestinal Mucosal Problems Classification with Deep Learning—0
FEED: Feature-level Ensemble Effect for knowledge Distillation—0
Geometric Framework for Cell Oversegmentation—0
Few-shot Adaptive Object Detection with Cross-Domain CutMix—0
Guided Recommendation for Model Fine-Tuning—0
Decoding Working-Memory Load During n-Back Task Performance from High Channel NIRS Data—0
Automated Scoring of Clinical Expressive Language Evaluation Tasks—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