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 9151–9175 of 10307 papers

TitleStatusHype
CleverDistiller: Simple and Spatially Consistent Cross-modal Distillation—0
CLICKER: Attention-Based Cross-Lingual Commonsense Knowledge Transfer—0
Client Clustering Meets Knowledge Sharing: Enhancing Privacy and Robustness in Personalized Peer-to-Peer Learning—0
Cliff-Learning—0
Boosting Transformers for Job Expression Extraction and Classification in a Low-Resource Setting—0
Clinical Concept and Relation Extraction Using Prompt-based Machine Reading Comprehension—0
Clinical Document Classification Using Labeled and Unlabeled Data Across Hospitals—0
Clinical Risk Prediction Using Language Models: Benefits And Considerations—0
Boosting the Convergence of Reinforcement Learning-based Auto-pruning Using Historical Data—0
CLIP also Understands Text: Prompting CLIP for Phrase Understanding—0
CLIP-aware Domain-Adaptive Super-Resolution—0
Symbol Correctness in Deep Neural Networks Containing Symbolic Layers—0
CLIP-CID: Efficient CLIP Distillation via Cluster-Instance Discrimination—0
Boosting Template-based SSVEP Decoding by Cross-domain Transfer Learning—0
CLIP-FLow: Contrastive Learning by semi-supervised Iterative Pseudo labeling for Optical Flow Estimation—0
CLIP is Almost All You Need: Towards Parameter-Efficient Scene Text Retrieval without OCR—0
Boosting Single-Frame 3D Object Detection by Simulating Multi-Frame Point Clouds—0
Boosting Self-Supervised Learning via Knowledge Transfer—0
Boosting Personalised Musculoskeletal Modelling with Physics-informed Knowledge Transfer—0
CLIP-S^4: Language-Guided Self-Supervised Semantic Segmentation—0
CLIP-S4: Language-Guided Self-Supervised Semantic Segmentation—0
Boosting pathology detection in infants by deep transfer learning from adult speech—0
QueryForm: A Simple Zero-shot Form Entity Query Framework—0
Boosting offline handwritten text recognition in historical documents with few labeled lines—0
Syn2Real Transfer Learning for Image Deraining Using Gaussian Processes—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