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 7001–7050 of 10307 papers

TitleStatusHype
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
LLEDA -- Lifelong Self-Supervised Domain Adaptation—0
LLM4WM: Adapting LLM for Wireless Multi-Tasking—0
LLM-KT: A Versatile Framework for Knowledge Transfer from Large Language Models to Collaborative Filtering—0
LLMs Are Globally Multilingual Yet Locally Monolingual: Exploring Knowledge Transfer via Language and Thought Theory—0
LLM-USO: Large Language Model-based Universal Sizing Optimizer—0
LMVE at SemEval-2020 Task 4: Commonsense Validation and Explanation using Pretraining Language Model—0
Local Herb Identification Using Transfer Learning: A CNN-Powered Mobile Application for Nepalese Flora—0
Localized Flood DetectionWith Minimal Labeled Social Media Data Using Transfer Learning—0
Localizing the conceptual difference of two scenes using deep learning for house keeping usages—0
A Locally Adaptive Algorithm for Multiple Testing with Network Structure—0
Local Motion Planner for Autonomous Navigation in Vineyards with a RGB-D Camera-Based Algorithm and Deep Learning Synergy—0
Local Rose Breeds Detection System Using Transfer Learning Techniques—0
Local Similarity-Aware Deep Feature Embedding—0
Local transfer learning from one data space to another—0
Local transfer learning Gaussian process modeling, with applications to surrogate modeling of expensive computer simulators—0
Local vs. Global: Local Land-Use and Land-Cover Models Deliver Higher Quality Maps—0
Location based Probabilistic Load Forecasting of EV Charging Sites: Deep Transfer Learning with Multi-Quantile Temporal Convolutional Network—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