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 6976–7000 of 10307 papers

TitleStatusHype
Cognitive simulation models for inertial confinement fusion: Combining simulation and experimental data—0
Domain Generalization using Ensemble Learning—0
Constructive and Toxic Speech Detection for Open-domain Social Media Comments in Vietnamese—0
Deep Learning-based Extreme Heatwave Forecast—0
Towards Few-Shot Fact-Checking via Perplexity—0
Generation of Realistic Cloud Access Times for Mobile Application Testing using Transfer Learning—0
Distance Metric-Based Learning with Interpolated Latent Features for Location Classification in Endoscopy Image and Video—0
Online Learning with Radial Basis Function Networks—0
Improving Generalization of Transfer Learning Across Domains Using Spatio-Temporal Features in Autonomous Driving—0
Transfer Learning for Automated Test Case Prioritization Using XCSFCode0
Unsupervised Transfer Learning in Multilingual Neural Machine Translation with Cross-Lingual Word Embeddings—0
Does the Magic of BERT Apply to Medical Code Assignment? A Quantitative Study—0
Energy Decay Network (EDeN)—0
Model-Agnostic Meta-Learning for EEG Motor Imagery Decoding in Brain-Computer-Interfacing—0
Best of Both Worlds: Robust Accented Speech Recognition with Adversarial Transfer Learning—0
Inter-subject Deep Transfer Learning for Motor Imagery EEG Decoding—0
Revisiting Model's Uncertainty and Confidences for Adversarial Example DetectionCode0
The Common Intuition to Transfer Learning Can Win or Lose: Case Studies for Linear Regression—0
Uncertainty-aware Incremental Learning for Multi-organ Segmentation—0
Deep Transfer Learning for WiFi Localization—0
A Taxonomy of Similarity Metrics for Markov Decision Processes—0
AfriVEC: Word Embedding Models for African Languages. Case Study of Fon and NobiinCode0
Deep Transfer Learning for Infectious Disease Case Detection Using Electronic Medical Records—0
The Effect of Q-function Reuse on the Total Regret of Tabular, Model-Free, Reinforcement Learning—0
Multitasking Deep Learning Model for Detection of Five Stages of Diabetic Retinopathy—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