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 5326–5350 of 10307 papers

TitleStatusHype
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