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 6451–6475 of 10307 papers

TitleStatusHype
In-Context Operator Learning for Linear Propagator Models—0
Semantic Segmentation on Remotely Sensed Images Using an Enhanced Global Convolutional Network with Channel Attention and Domain Specific Transfer Learning—0
Incorporating Domain Knowledge into Health Recommender Systems using Hyperbolic Embeddings—0
Incorporating Ensemble and Transfer Learning For An End-To-End Auto-Colorized Image Detection Model—0
Increasing Shape Bias in ImageNet-Trained Networks Using Transfer Learning and Domain-Adversarial Methods—0
Semantic Segmentation Using Transfer Learning on Fisheye Images—0
Incremental Feature Learning For Infinite Data—0
Incremental Learning in Deep Convolutional Neural Networks Using Partial Network Sharing—0
Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation—0
Incremental Learning with Maximum Entropy Regularization: Rethinking Forgetting and Intransigence—0
Incrementally Learning Multiple Diverse Data Domains via Multi-Source Dynamic Expansion Model—0
Applying Deep Neural Networks to automate visual verification of manual bracket installations in aerospace—0
A Computational Approach to Understand Mental Health from Reddit: Knowledge-aware Multitask Learning Framework—0
sEMG-based Fine-grained Gesture Recognition via Improved LightGBM Model—0
SEMI-CenterNet: A Machine Learning Facilitated Approach for Semiconductor Defect Inspection—0
IndicBART: A Pre-trained Model for Indic Natural Language Generation—0
Applied Computer Vision on 2-Dimensional Lung X-Ray Images for Assisted Medical Diagnosis of Pneumonia—0
Semi Few-Shot Attribute Translation—0
TenTrans Multilingual Low-Resource Translation System for WMT21 Indo-European Languages Task—0
Indiscriminate Data Poisoning Attacks on Pre-trained Feature Extractors—0
Individual Fairness Through Reweighting and Tuning—0
Text-to-speech for the hearing impaired—0
A Comprehensive Survey on Source-free Domain Adaptation—0
A Survey on Curriculum Learning—0
Indoor Localization Under Limited Measurements: A Cross-Environment Joint Semi-Supervised and Transfer Learning Approach—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