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 3801–3825 of 10307 papers

TitleStatusHype
Topological Learning for Motion Data via Mixed CoordinatesCode0
Adapter Pruning using Tropical Characterization—0
A foundational neural operator that continuously learns without forgetting—0
Transfer Learning in Transformer-Based Demand Forecasting For Home Energy Management System—0
RCKD: Response-Based Cross-Task Knowledge Distillation for Pathological Image Analysis—0
Automaton Distillation: Neuro-Symbolic Transfer Learning for Deep Reinforcement Learning—0
QWID: Quantized Weed Identification Deep neural networkCode0
A transfer learning approach with convolutional neural network for Face Mask Detection—0
ODM3D: Alleviating Foreground Sparsity for Semi-Supervised Monocular 3D Object DetectionCode0
Boosting Data Analytics With Synthetic Volume ExpansionCode0
Parameter-Efficient Methods for Metastases Detection from Clinical Notes—0
Large-scale Foundation Models and Generative AI for BigData Neuroscience—0
Transductive conformal inference with adaptive scoresCode0
MELEP: A Novel Predictive Measure of Transferability in Multi-Label ECG DiagnosisCode0
Fantastic Gains and Where to Find Them: On the Existence and Prospect of General Knowledge Transfer between Any Pretrained ModelCode0
Can LLMs Grade Short-Answer Reading Comprehension Questions : An Empirical Study with a Novel Dataset—0
De-novo Chemical Reaction Generation by Means of Temporal Convolutional Neural Networks—0
Unleashing the potential of GNNs via Bi-directional Knowledge Transfer—0
Feature Extraction and Classification from Planetary Science Datasets enabled by Machine Learning—0
Learning Transfers over Several Programming Languages—0
Transfer of Reinforcement Learning-Based Controllers from Model- to Hardware-in-the-Loop—0
An Efficient Deep Learning-based approach for Recognizing Agricultural Pests in the Wild—0
Deep machine learning for meteor monitoring: advances with transfer learning and gradient-weighted class activation mapping—0
Transferring a molecular foundation model for polymer property predictions—0
Combining Behaviors with the Successor Features Keyboard—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