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 6701–6750 of 10307 papers

TitleStatusHype
Robust Speech and Natural Language Processing Models for Depression Screening—0
Robust Tickets Can Transfer Better: Drawing More Transferable Subnetworks in Transfer Learning—0
Robust Transfer Learning for Active Level Set Estimation with Locally Adaptive Gaussian Process Prior—0
Robust Transfer Learning with Pretrained Language Models through Adapters—0
Robust Transfer Learning with Unreliable Source Data—0
Robust Visual Knowledge Transfer via EDA—0
Role of Mixup in Topological Persistence Based Knowledge Distillation for Wearable Sensor Data—0
RotNet: Fast and Scalable Estimation of Stellar Rotation Periods Using Convolutional Neural Networks—0
Roulette: A Semantic Privacy-Preserving Device-Edge Collaborative Inference Framework for Deep Learning Classification Tasks—0
Rover: An online Spark SQL tuning service via generalized transfer learning—0
RRWaveNet: A Compact End-to-End Multi-Scale Residual CNN for Robust PPG Respiratory Rate Estimation—0
RS-FME-SwinT: A Novel Feature Map Enhancement Framework Integrating Customized SwinT with Residual and Spatial CNN for Monkeypox Diagnosis—0
RUBERT: A Bilingual Roman Urdu BERT Using Cross Lingual Transfer Learning—0
Rumour Detection via Zero-shot Cross-lingual Transfer Learning—0
Russo-Ukrainian war disinformation detection in suspicious Telegram channels—0
S2R-ViT for Multi-Agent Cooperative Perception: Bridging the Gap from Simulation to Reality—0
S^4-Tuning: A Simple Cross-lingual Sub-network Tuning Method—0
S^4-Tuning: A Simple Cross-lingual Sub-network Tuning Method—0
S-Adapter: Generalizing Vision Transformer for Face Anti-Spoofing with Statistical Tokens—0
SPD-CFL: Stepwise Parameter Dropout for Efficient Continual Federated Learning—0
SafeNet: The Unreasonable Effectiveness of Ensembles in Private Collaborative Learning—0
SAFFRON: tranSfer leArning For Food-disease RelatiOn extractioN—0
SA-GDA: Spectral Augmentation for Graph Domain Adaptation—0
SAJA at TRAC 2020 Shared Task: Transfer Learning for Aggressive Identification with XGBoost—0
Salienteye: Maximizing Engagement While Maintaining Artistic Style on Instagram Using Deep Neural Networks—0
Learning to Adapt SAM for Segmenting Cross-domain Point Clouds—0
SAM-Lightening: A Lightweight Segment Anything Model with Dilated Flash Attention to Achieve 30 times Acceleration—0
Sample-based Regularization: A Transfer Learning Strategy Toward Better Generalization—0
Sample-Efficient Bayesian Transfer Learning for Online Machine Parameter Optimization—0
Sample-Efficient Deep Learning for COVID-19 Diagnosis Based on CT Scans—0
Sample-Efficient Reinforcement Learning through Transfer and Architectural Priors—0
Sampling to Distill: Knowledge Transfer from Open-World Data—0
Sarcasm Detection as a Catalyst: Improving Stance Detection with Cross-Target Capabilities—0
SASSL: Enhancing Self-Supervised Learning via Neural Style Transfer—0
Say What? Collaborative Pop Lyric Generation Using Multitask Transfer Learning—0
SB-MTL: Score-based Meta Transfer-Learning for Cross-Domain Few-Shot Learning—0
SB_NITK at MEDIQA 2021: Leveraging Transfer Learning for Question Summarization in Medical Domain—0
Scalable and Interpretable Contextual Bandits: A Literature Review and Retail Offer Prototype—0
Scalable and reliable deep transfer learning for intelligent fault detection via multi-scale neural processes embedded with knowledge—0
Scalable Cross-Lingual Transfer of Neural Sentence Embeddings—0
Scalable Differential Privacy With Sparse Network Finetuning—0
Scalable Forward-Forward Algorithm—0
Scalable Greedy Algorithms for Transfer Learning—0
Scalable handwritten text recognition system for lexicographic sources of under-resourced languages and alphabets—0
Scalable Hyperparameter Transfer Learning—0
Scalable Learning of Segment-Level Traffic Congestion Functions—0
Scalable Multi-Task Gaussian Processes with Neural Embedding of Coregionalization—0
Scalable Multi-Task Transfer Learning for Molecular Property Prediction—0
Scalable Neural Data Server: A Data Recommender for Transfer Learning—0
Scalable Transfer Learning with Expert Models—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