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 7201–7225 of 10307 papers

TitleStatusHype
Towards Zero-Shot Knowledge Distillation for Natural Language Processing—0
Universal Adaptive Control of Nonlinear Systems—0
Advances in deep learning methods for pavement surface crack detection and identification with visible light visual imagesCode0
Comparison of different CNNs for breast tumor classification from ultrasound images—0
Screening COVID-19 Based on CT/CXR Images & Building a Publicly Available CT-scan Dataset of COVID-19—0
Ranking and Rejecting of Pre-Trained Deep Neural Networks in Transfer Learning based on Separation Index—0
PaXNet: Dental Caries Detection in Panoramic X-ray using Ensemble Transfer Learning and Capsule Classifier—0
Towards a Universal Continuous Knowledge Base—0
Evolutionary Gait Transfer of Multi-Legged Robots in Complex Terrains—0
Decentralized Federated Learning via Mutual Knowledge Transfer—0
Cross-lingual Universal Dependency Parsing Only from One Monolingual Treebank—0
Seed Phenotyping on Neural Networks using Domain Randomization and Transfer Learning—0
Diabetic Retinopathy Grading System Based on Transfer Learning—0
Adversarial Meta Sampling for Multilingual Low-Resource Speech Recognition—0
Objective Evaluation of Deep Uncertainty Predictions for COVID-19 Detection—0
A Review of Artificial Intelligence Technologies for Early Prediction of Alzheimer's Disease—0
MailLeak: Obfuscation-Robust Character Extraction Using Transfer Learning—0
Flexible deep transfer learning by separate feature embeddings and manifold alignment—0
Unboxing Engagement in YouTube Influencer Videos: An Attention-Based Approach—0
Cross-Domain Latent Modulation for Variational Transfer Learning—0
Knowledge Transfer Based Fine-grained Visual ClassificationCode0
Domain-adaptive Fall Detection Using Deep Adversarial Training—0
Breaking Writer's Block: Low-cost Fine-tuning of Natural Language Generation Models—0
Exploring Fluent Query Reformulations with Text-to-Text Transformers and Reinforcement Learning—0
Deep learning and high harmonic generation—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