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 1826–1850 of 10307 papers

TitleStatusHype
Client Clustering Meets Knowledge Sharing: Enhancing Privacy and Robustness in Personalized Peer-to-Peer Learning—0
Cliff-Learning—0
Apple Leaf Disease Identification through Region-of-Interest-Aware Deep Convolutional Neural Network—0
Clinical Concept and Relation Extraction Using Prompt-based Machine Reading Comprehension—0
Clinical Document Classification Using Labeled and Unlabeled Data Across Hospitals—0
Convolutional Neural Network for Stereotypical Motor Movement Detection in Autism—0
Adversarial Multi-Agent Reinforcement Learning for Proactive False Data Injection Detection—0
Active Adversarial Domain Adaptation—0
CLIP-aware Domain-Adaptive Super-Resolution—0
Chimpanzee voice prints? Insights from transfer learning experiments from human voices—0
CLIP-CID: Efficient CLIP Distillation via Cluster-Instance Discrimination—0
ChildGAN: Large Scale Synthetic Child Facial Data Using Domain Adaptation in StyleGAN—0
A Point in the Right Direction: Vector Prediction for Spatially-aware Self-supervised Volumetric Representation Learning—0
CLIP is Almost All You Need: Towards Parameter-Efficient Scene Text Retrieval without OCR—0
Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an example—0
Convolutional neural network for Lyman break galaxies classification and redshift regression in DESI (Dark Energy Spectroscopic Instrument)—0
CheX-Nomaly: Segmenting Lung Abnormalities from Chest Radiographs using Machine Learning—0
CLIP-S^4: Language-Guided Self-Supervised Semantic Segmentation—0
CLIP-S4: Language-Guided Self-Supervised Semantic Segmentation—0
Adversarial Meta Sampling for Multilingual Low-Resource Speech Recognition—0
A Question Answering Based Pipeline for Comprehensive Chinese EHR Information Extraction—0
A Physics-preserved Transfer Learning Method for Differential Equations—0
Chest Disease Detection In X-Ray Images Using Deep Learning Classification Method—0
Close Yet Distinctive Domain Adaptation—0
Action Recognition using Transfer Learning and Majority Voting for CSGO—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