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 6401–6450 of 10307 papers

TitleStatusHype
Learn Continually, Generalize Rapidly: Lifelong Knowledge Accumulation for Few-shot LearningCode0
Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine TranslationCode1
On Training Sketch Recognizers for New Domains—0
Group-Sparse Matrix Factorization for Transfer Learning of Word Embeddings—0
Improving Zero-Shot Cross-Lingual Transfer Learning via Robust TrainingCode0
Age Range Estimation using MTCNN and VGG-Face Model—0
Identifying the Limits of Cross-Domain Knowledge Transfer for Pretrained ModelsCode0
AMMU : A Survey of Transformer-based Biomedical Pretrained Language Models—0
Neural Transfer Learning for Repairing Security Vulnerabilities in C CodeCode1
MetaXL: Meta Representation Transformation for Low-resource Cross-lingual LearningCode1
Does language help generalization in vision models?Code0
To Share or not to Share: Predicting Sets of Sources for Model Transfer LearningCode0
What to Pre-Train on? Efficient Intermediate Task SelectionCode1
Personalized Semi-Supervised Federated Learning for Human Activity Recognition—0
Assessment of deep learning based blood pressure prediction from PPG and rPPG signals—0
Adaptive Sparse Transformer for Multilingual Translation—0
Do Deep Neural Networks Forget Facial Action Units? -- Exploring the Effects of Transfer Learning in Health Related Facial Expression Recognition—0
XTREME-R: Towards More Challenging and Nuanced Multilingual EvaluationCode1
Demystifying BERT: Implications for Accelerator Design—0
Leveraging Label Information in a Knowledge-Driven Approach for Rolling-Element Bearings Remaining Useful Life PredictionCode0
WiFiNet: WiFi-based indoor localisation using CNNs—0
Multilingual Transfer Learning for Code-Switched Language and Speech Neural Modeling—0
Fruit Quality and Defect Image Classification with Conditional GAN Data AugmentationCode1
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
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
eGAN: Unsupervised approach to class imbalance using transfer learningCode0
DenResCov-19: A deep transfer learning network for robust automatic classification of COVID-19, pneumonia, and tuberculosis from X-rays—0
Post-Hoc Domain Adaptation via Guided Data HomogenizationCode0
Emotion Recognition from Speech Using Wav2vec 2.0 EmbeddingsCode1
CutPaste: Self-Supervised Learning for Anomaly Detection and LocalizationCode1
Grapheme-to-Phoneme Transformer Model for Transfer Learning Dialects—0
A transfer-learning approach for lesion detection in endoscopic images from the urinary tract—0
Affordance Transfer Learning for Human-Object Interaction DetectionCode1
Analysis Towards Classification of Infection and Ischaemia of Diabetic Foot Ulcers—0
Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark—0
Tuned Compositional Feature Replays for Efficient Stream LearningCode0
Adaptive Variants of Optimal Feedback Policies—0
Efficient transfer learning for NLP with ELECTRACode1
CodeTrans: Towards Cracking the Language of Silicon's Code Through Self-Supervised Deep Learning and High Performance ComputingCode1
A Concise Review of Transfer Learning—0
SpeechStew: Simply Mix All Available Speech Recognition Data to Train One Large Neural Network—0
Acted vs. Improvised: Domain Adaptation for Elicitation Approaches in Audio-Visual Emotion Recognition—0
Efficient Personalized Speech Enhancement through Self-Supervised Learning—0
Graph Sampling Based Deep Metric Learning for Generalizable Person Re-Identification—0
TATL: Task Agnostic Transfer Learning for Skin Attributes Detection—0
Few-Shot Keyword Spotting in Any LanguageCode1
Using GPT-2 to Create Synthetic Data to Improve the Prediction Performance of NLP Machine Learning Classification Models—0
On the Pitfalls of Learning with Limited Data: A Facial Expression Recognition Case Study—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