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 6901–6925 of 10307 papers

TitleStatusHype
WiFiNet: WiFi-based indoor localisation using CNNs—0
Multilingual Transfer Learning for Code-Switched Language and Speech Neural Modeling—0
Transfer Learning for Neural Networks-based Equalizers in Coherent Optical Systems—0
Learning from 2D: Contrastive Pixel-to-Point Knowledge Transfer for 3D Pretraining—0
eGAN: Unsupervised approach to class imbalance using transfer learningCode0
Detecting False Data Injection Attacks in Smart Grids with Modeling Errors: A Deep Transfer Learning Based ApproachCode0
The NTNU Taiwanese ASR System for Formosa Speech Recognition Challenge 2020—0
Post-Hoc Domain Adaptation via Guided Data HomogenizationCode0
DenResCov-19: A deep transfer learning network for robust automatic classification of COVID-19, pneumonia, and tuberculosis from X-rays—0
A transfer-learning approach for lesion detection in endoscopic images from the urinary tract—0
Grapheme-to-Phoneme Transformer Model for Transfer Learning Dialects—0
Analysis Towards Classification of Infection and Ischaemia of Diabetic Foot Ulcers—0
Adaptive Variants of Optimal Feedback Policies—0
Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark—0
Tuned Compositional Feature Replays for Efficient Stream LearningCode0
Efficient Personalized Speech Enhancement through Self-Supervised Learning—0
SpeechStew: Simply Mix All Available Speech Recognition Data to Train One Large Neural Network—0
A Concise Review of Transfer Learning—0
Acted vs. Improvised: Domain Adaptation for Elicitation Approaches in Audio-Visual Emotion Recognition—0
Graph Sampling Based Deep Metric Learning for Generalizable Person Re-Identification—0
TATL: Task Agnostic Transfer Learning for Skin Attributes Detection—0
COVID-19 Detection in Cough, Breath and Speech using Deep Transfer Learning and Bottleneck Features—0
On the Pitfalls of Learning with Limited Data: A Facial Expression Recognition Case Study—0
Using GPT-2 to Create Synthetic Data to Improve the Prediction Performance of NLP Machine Learning Classification Models—0
Towards Offensive Language Identification for Dravidian LanguagesCode0
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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