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 5301–5350 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
Lifelong Reinforcement Learning with Temporal Logic Formulas and Reward Machines—0
Lifelong Sequence Generation with Dynamic Module Expansion and Adaptation—0
LIFT-SLAM: a deep-learning feature-based monocular visual SLAM method—0
Light3DPose: Real-time Multi-Person 3D PoseEstimation from Multiple Views—0
Lightweight Convolutional Neural Networks for Retinal Disease Classification—0
Lightweight Encoder-Decoder Architecture for Foot Ulcer Segmentation—0
Light-weight Head Pose Invariant Gaze Tracking—0
Limitations of Knowledge Distillation for Zero-shot Transfer Learning—0
Limits of Model Selection under Transfer Learning—0
Limits of Transfer Learning—0
Linguist Geeks on WNUT-2020 Task 2: COVID-19 Informative Tweet Identification using Progressive Trained Language Models and Data Augmentation—0
Linguistic approach based Transfer Learning for Sentiment Classification in Hindi—0
Linguistic Considerations in Automatic Question Generation—0
Discovering Salient Neurons in Deep NLP Models—0
Linguistic Knowledge Transfer Learning for Speech Enhancement—0
Linked Adapters: Linking Past and Future to Present for Effective Continual Learning—0
An Empirical Study on Data Leakage and Generalizability of Link Prediction Models for Issues and Commits—0
LinkSAGE: Optimizing Job Matching Using Graph Neural Networks—0
LION: Implicit Vision Prompt Tuning—0
LipidBERT: A Lipid Language Model Pre-trained on METiS de novo Lipid Library—0
LIRMM-Advanse at SemEval-2019 Task 3: Attentive Conversation Modeling for Emotion Detection and Classification—0
Listening to the World Improves Speech Command Recognition—0
Efficient Systematic Reviews: Literature Filtering with Transformers & Transfer Learning—0
Live American Sign Language Letter Classification with Convolutional Neural Networks—0
Liver Steatosis Segmentation with Deep Learning Methods—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