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 5301–5325 of 10307 papers

TitleStatusHype
Leveraging Non-Conversational Tasks for Low Resource Slot Filling: Does it help?—0
Leveraging Parameter-Efficient Transfer Learning for Multi-Lingual Text-to-Speech Adaptation—0
Leveraging Pre-trained AudioLDM for Sound Generation: A Benchmark Study—0
On the Benefits of Public Representations for Private Transfer Learning under Distribution Shift—0
Leveraging Road Area Semantic Segmentation with Auxiliary Steering Task—0
Leveraging Seq2seq Language Generation for Multi-level Product Issue Identification—0
Leveraging Speech PTM, Text LLM, and Emotional TTS for Speech Emotion Recognition—0
Leveraging Text Data Using Hybrid Transformer-LSTM Based End-to-End ASR in Transfer Learning—0
Leveraging Transfer Learning and User-Specific Updates for Rapid Training of BCI Decoders—0
Segmentation of Skin Lesions and their Attributes Using Multi-Scale Convolutional Neural Networks and Domain Specific Augmentations—0
Leveraging Transformers for StarCraft Macromanagement Prediction—0
Leveraging universality of jet taggers through transfer learning—0
Leveraging Unpaired Text Data for Training End-to-End Speech-to-Intent Systems—0
Leveraging Visual Knowledge in Language Tasks: An Empirical Study on Intermediate Pre-training for Cross-Modal Knowledge Transfer—0
Leveraging Visual Knowledge in Language Tasks: An Empirical Study on Intermediate Pre-training for Cross-modal Knowledge Transfer—0
Leveraging Weakly Annotated Data for Hate Speech Detection in Code-Mixed Hinglish: A Feasibility-Driven Transfer Learning Approach with Large Language Models—0
LIDSNet: A Lightweight on-device Intent Detection model using Deep Siamese Network—0
Lifelong Event Detection with Embedding Space Separation and Compaction—0
Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems—0
Lifelong Learning for Fog Load Balancing: A Transfer Learning Approach—0
Lifelong Learning using Eigentasks: Task Separation, Skill Acquisition, and Selective Transfer—0
Lifelong Learning using Eigentasks: Task Separation, Skill Acquisition and Selective Transfer—0
Lifelong Machine Learning for Topic Modeling and Beyond—0
Lifelong Pretraining: Continually Adapting Language Models to Emerging Corpora—0
Lifelong Reinforcement Learning with Similarity-Driven Weighting by Large 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