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 5126–5150 of 10307 papers

TitleStatusHype
Latent Intuitive Physics: Learning to Transfer Hidden Physics from A 3D Video—0
Latent Object Characteristics Recognition with Visual to Haptic-Audio Cross-modal Transfer Learning—0
Latent User Linking for Collaborative Cross Domain Recommendation—0
Lautum Regularization for Semi-supervised Transfer Learning—0
LaViP:Language-Grounded Visual Prompts—0
LAWDR: Language-Agnostic Weighted Document Representations from Pre-trained Models—0
Layer by Layer: Uncovering Where Multi-Task Learning Happens in Instruction-Tuned Large Language Models—0
Noisy Data Meets Privacy: Training Local Models with Post-Processed Remote Queries—0
Leaf Identification Using a Deep Convolutional Neural Network—0
LEAN: Light and Efficient Audio Classification Network—0
LEAPER: Fast and Accurate FPGA-based System Performance Prediction via Transfer Learning—0
LEAPME: Learning-based Property Matching with Embeddings—0
Learn and Transfer Knowledge of Preferred Assistance Strategies in Semi-autonomous Telemanipulation—0
Synthetic Data Can Also Teach: Synthesizing Effective Data for Unsupervised Visual Representation Learning—0
Learn Dynamic-Aware State Embedding for Transfer Learning—0
Learned 3D Shape Representations Using Fused Geometrically Augmented Images: Application to Facial Expression and Action Unit Detection—0
Learn Faster and Forget Slower via Fast and Stable Task Adaptation—0
Learn from Balance: Rectifying Knowledge Transfer for Long-Tailed Scenarios—0
Learn From the Past: Experience Ensemble Knowledge Distillation—0
Learning 3D Robotics Perception using Inductive Priors—0
Learning 4D Panoptic Scene Graph Generation from Rich 2D Visual Scene—0
Learning Abstract Concept Embeddings from Multi-Modal Data: Since You Probably Can't See What I Mean—0
Learning across label confidence distributions using Filtered Transfer Learning—0
Learning a Deep Compact Image Representation for Visual Tracking—0
Learning a Deep Model for Human Action Recognition from Novel Viewpoints—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