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 9651–9700 of 10307 papers

TitleStatusHype
Embedding Compression for Teacher-to-Student Knowledge Transfer—0
Embedding Semantic Similarity in Tree Kernels for Domain Adaptation of Relation Extraction—0
Embeddings models for Buddhist Sanskrit—0
Embed Everything: A Method for Efficiently Co-Embedding Multi-Modal Spaces—0
Embodied Multimodal Multitask Learning—0
Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning—0
EMGTFNet: Fuzzy Vision Transformer to decode Upperlimb sEMG signals for Hand Gestures Recognition—0
EMGTTL: Transformers-Based Transfer Learning for Classification of ADL using Raw Surface EMG Signals—0
Pay attention to emoji: Feature Fusion Network with EmoGraph2vec Model for Sentiment Analysis—0
Emotion Classification in Low and Moderate Resource Languages—0
Emotion Classification in Short English Texts using Deep Learning Techniques—0
Emotion Detection from EEG using Transfer Learning—0
Conversational Transfer Learning for Emotion Recognition—0
Emotion Recognition Using Fusion of Audio and Video Features—0
Empirical Evaluation of Knowledge Distillation from Transformers to Subquadratic Language Models—0
Empirical Evaluation of the Segment Anything Model (SAM) for Brain Tumor Segmentation—0
Empirical Gaussian priors for cross-lingual transfer learning—0
Empirically Measuring Transfer Distance for System Design and Operation—0
Empirical study of pretrained multilingual language models for zero-shot cross-lingual knowledge transfer in generation—0
Employing Federated Learning for Training Autonomous HVAC Systems—0
Employing High-Dimensional RIS Information for RIS-aided Localization Systems—0
Employing the Correspondence of Relations and Connectives to Identify Implicit Discourse Relations via Label Embeddings—0
Employing Two-Dimensional Word Embedding for Difficult Tabular Data Stream Classification—0
Empowering Agricultural Insights: RiceLeafBD - A Novel Dataset and Optimal Model Selection for Rice Leaf Disease Diagnosis through Transfer Learning Technique—0
Empowering COVID-19 Detection: Optimizing Performance Through Fine-Tuned EfficientNet Deep Learning Architecture—0
Empowering Long-tail Item Recommendation through Cross Decoupling Network (CDN)—0
Emulation Learning for Neuromimetic Systems—0
Enabling Asymmetric Knowledge Transfer in Multi-Task Learning with Self-Auxiliaries—0
Enabling Continual Learning in Neural Networks with Meta Learning—0
Enabling Deep Learning-based Physical-layer Secret Key Generation for FDD-OFDM Systems in Multi-Environments—0
Enabling Deep Learning on Edge Devices through Filter Pruning and Knowledge Transfer—0
Enabling hand gesture customization on wrist-worn devices—0
Enabling Incremental Knowledge Transfer for Object Detection at the Edge—0
Enabling Intelligent Vehicular Networks Through Distributed Learning in the Non-Terrestrial Networks 6G Vision—0
Enabling Low-Resource Transfer Learning across COVID-19 Corpora by Combining Event-Extraction and Co-Training—0
Enabling Multi-Agent Transfer Reinforcement Learning via Scenario Independent Representation—0
Encoder-Decoder Framework for Interactive Free Verses with Generation with Controllable High-Quality Rhyming—0
Meta-models for transfer learning in source localisation—0
Encoding Explanatory Knowledge for Zero-shot Science Question Answering—0
End-to-End 3D-PointCloud Semantic Segmentation for Autonomous Driving—0
End-to-end acoustic modelling for phone recognition of young readers—0
End-to-End Deep Neural Networks and Transfer Learning for Automatic Analysis of Nation-State Malware—0
End-to-End Deep Transfer Learning for Calibration-free Motor Imagery Brain Computer Interfaces—0
End-to-End Diarization for Variable Number of Speakers with Local-Global Networks and Discriminative Speaker Embeddings—0
End-to-End Framework for Predicting the Remaining Useful Life of Lithium-Ion Batteries—0
End-to-End Multi-Speaker Speech Recognition using Speaker Embeddings and Transfer Learning—0
End-to-End Neural Network Compression via _1_2 Regularized Latency Surrogates—0
End-to-End Speech Translation of Arabic to English Broadcast News—0
End-to-End Speech-Translation with Knowledge Distillation: FBK@IWSLT2020—0
End-to-end Spoken Conversational Question Answering: Task, Dataset and Model—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