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 5476–5500 of 10307 papers

TitleStatusHype
A Look at Value-Based Decision-Time vs. Background Planning Methods Across Different Settings—0
Multi scale Feature Extraction and Fusion for Online Knowledge Distillation—0
Longitudinal detection of new MS lesions using Deep Learning—0
DRAFT: A Novel Framework to Reduce Domain Shifting in Self-supervised Learning and Its Application to Children's ASR—0
Assessing the Value of Transfer Learning Metrics for RF Domain Adaptation—0
Towards Diverse Evaluation of Class Incremental Learning: A Representation Learning Perspective—0
Recursive Neural Programs: Variational Learning of Image Grammars and Part-Whole Hierarchies—0
Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification—0
Subsurface Depths Structure Maps Reconstruction with Generative Adversarial Networks—0
A Novel Implementation of Machine Learning for the Efficient, Explainable Diagnosis of COVID-19 from Chest CT—0
Semantic-Discriminative Mixup for Generalizable Sensor-based Cross-domain Activity Recognition—0
Residual-based physics-informed transfer learning: A hybrid method for accelerating long-term CFD simulations via deep learning—0
Quantitative Imaging Principles Improves Medical Image LearningCode0
FreeTransfer-X: Safe and Label-Free Cross-Lingual Transfer from Off-the-Shelf Models—0
FreeKD: Free-direction Knowledge Distillation for Graph Neural Networks—0
Tackling Data Scarcity with Transfer Learning: A Case Study of Thickness Characterization from Optical Spectra of Perovskite Thin Films—0
TransRec: Learning Transferable Recommendation from Mixture-of-Modality Feedback—0
Anomaly Detection and Inter-Sensor Transfer Learning on Smart Manufacturing Datasets—0
DeepEmotex: Classifying Emotion in Text Messages using Deep Transfer Learning—0
PAC-Net: A Model Pruning Approach to Inductive Transfer Learning—0
Modeling Generalized Specialist Approach To Train Quality Resilient Snapshot EnsembleCode0
Convex Hull Prediction for Adaptive Video Streaming by Recurrent Learning—0
A Correlation-Ratio Transfer Learning and Variational Stein's Paradox—0
Toward Dynamic Stability Assessment of Power Grid Topologies using Graph Neural NetworksCode0
On Hypothesis Transfer Learning of Functional Linear Models—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