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 8326–8350 of 10307 papers

TitleStatusHype
SAR: Scale-Aware Restoration Learning for 3D Tumor Segmentation—0
Universal Neural Machine Translation for Extremely Low Resource Languages—0
Universal-RCNN: Universal Object Detector via Transferable Graph R-CNN—0
Universal Recurrent Neural Network Grammar—0
Universal Sentence Encoder for English—0
Universal Spam Detection using Transfer Learning of BERT Model—0
Universal Successor Features Based Deep Reinforcement Learning for Navigation—0
Universal Successor Representations for Transfer Reinforcement Learning—0
Unlabeled Data Deployment for Classification of Diabetic Retinopathy Images Using Knowledge Transfer—0
Unleashing the potential of GNNs via Bi-directional Knowledge Transfer—0
Unleashing the Potential of Mamba: Boosting a LiDAR 3D Sparse Detector by Using Cross-Model Knowledge Distillation—0
Unleashing the Power of Transfer Learning Model for Sophisticated Insect Detection: Revolutionizing Insect Classification—0
Unlocking large-scale crop field delineation in smallholder farming systems with transfer learning and weak supervision—0
Unlocking the Transferability of Tokens in Deep Models for Tabular Data—0
Unlocking Transfer Learning for Open-World Few-Shot Recognition—0
Unlock the Power: Competitive Distillation for Multi-Modal Large Language Models—0
Unmasking unlearnable models: a classification challenge for biomedical images without visible cues—0
Unregulated Chinese-to-English Data Expansion Does NOT Work for Neural Event Detection—0
Unseen Object Instance Segmentation with Fully Test-time RGB-D Embeddings Adaptation—0
Unsupervised Alignment of Distributional Word Embeddings—0
Unsupervised Audio-Visual Subspace Alignment for High-Stakes Deception Detection—0
Unsupervised Cross-Dataset Transfer Learning for Person Re-Identification—0
Unsupervised Cross-Domain Regression for Fine-grained 3D Game Character Reconstruction—0
Unsupervised Data Validation Methods for Efficient Model Training—0
Unsupervised Deep Feature Transfer for Low Resolution Image Classification—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