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 2601–2625 of 10307 papers

TitleStatusHype
ODM3D: Alleviating Foreground Sparsity for Semi-Supervised Monocular 3D Object DetectionCode0
MELEP: A Novel Predictive Measure of Transferability in Multi-Label ECG DiagnosisCode0
Large-scale Foundation Models and Generative AI for BigData Neuroscience—0
Transductive conformal inference with adaptive scoresCode0
Boosting Data Analytics With Synthetic Volume ExpansionCode0
Parameter-Efficient Methods for Metastases Detection from Clinical Notes—0
CPIA Dataset: A Comprehensive Pathological Image Analysis Dataset for Self-supervised Learning Pre-trainingCode1
Feature Extraction and Classification from Planetary Science Datasets enabled by Machine Learning—0
Can LLMs Grade Short-Answer Reading Comprehension Questions : An Empirical Study with a Novel Dataset—0
Deep Learning on SAR Imagery: Transfer Learning Versus Randomly Initialized WeightsCode1
Fantastic Gains and Where to Find Them: On the Existence and Prospect of General Knowledge Transfer between Any Pretrained ModelCode0
Unleashing the potential of GNNs via Bi-directional Knowledge Transfer—0
De-novo Chemical Reaction Generation by Means of Temporal Convolutional Neural Networks—0
PETA: Evaluating the Impact of Protein Transfer Learning with Sub-word Tokenization on Downstream ApplicationsCode1
Transfer of Reinforcement Learning-Based Controllers from Model- to Hardware-in-the-Loop—0
Transferring a molecular foundation model for polymer property predictions—0
An Efficient Deep Learning-based approach for Recognizing Agricultural Pests in the Wild—0
Learning Transfers over Several Programming Languages—0
CommonCanvas: An Open Diffusion Model Trained with Creative-Commons ImagesCode2
Deep machine learning for meteor monitoring: advances with transfer learning and gradient-weighted class activation mapping—0
LoRAShear: Efficient Large Language Model Structured Pruning and Knowledge RecoveryCode1
Deep Learning Models for Classification of COVID-19 Cases by Medical Images—0
Transfer learning for day-ahead load forecasting: a case study on European national electricity demand time seriesCode0
Retrieval-based Knowledge Transfer: An Effective Approach for Extreme Large Language Model Compression—0
Mean Teacher DETR with Masked Feature Alignment: A Robust Domain Adaptive Detection Transformer Framework—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