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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 7251–7275 of 10307 papers

TitleStatusHype
LMVE at SemEval-2020 Task 4: Commonsense Validation and Explanation using Pretraining Language Model—0
An End-to-End Attack on Text-based CAPTCHAs Based on Cycle-Consistent Generative Adversarial Network—0
Supervised domain adaptation for building extraction from off-nadir aerial images—0
SLABERT Talk Pretty One Day: Modeling Second Language Acquisition with BERT—0
Local Herb Identification Using Transfer Learning: A CNN-Powered Mobile Application for Nepalese Flora—0
Slash or burn: Power line and vegetation classification for wildfire prevention—0
A Comparative Evaluation Of Transformer Models For De-Identification Of Clinical Text Data—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
Sleep Position Classification using Transfer Learning for Bed-based Pressure Sensors—0
An Empirical Study on Transfer Learning for Privilege Review—0
Logical Activation Functions: Logit-space equivalents of Probabilistic Boolean Operators—0
LogitMat : Zeroshot Learning Algorithm for Recommender Systems without Transfer Learning or Pretrained Models—0
Logits Poisoning Attack in Federated Distillation—0
An Empirical Study on the Transferability of Transformer Modules in Parameter-Efficient Fine-Tuning—0
Logos as a Well-Tempered Pre-train for Sign Language Recognition—0
LokiTalk: Learning Fine-Grained and Generalizable Correspondences to Enhance NeRF-based Talking Head Synthesis—0
Show:102550
← PrevPage 291 of 413Next →
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