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 5351–5375 of 10307 papers

TitleStatusHype
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
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
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
Longitudinal detection of new MS lesions using Deep Learning—0
Long-tail Recognition via Compositional Knowledge Transfer—0
Data-Efficient Pretraining via Contrastive Self-Supervision—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