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 6926–6950 of 10307 papers

TitleStatusHype
Distance Metric-Based Learning with Interpolated Latent Features for Location Classification in Endoscopy Image and Video—0
An Occam's Razor View on Learning Audiovisual Emotion Recognition with Small Training Sets—0
Learning a functional control for high-frequency finance—0
Robust and flexible learning of a high-dimensional classification rule using auxiliary outcomes—0
Learning and Knowledge Transfer with Memory Networks for Machine Comprehension—0
Learning a Non-Linear Knowledge Transfer Model for Cross-View Action Recognition—0
Learning Answer Embeddings for Visual Question Answering—0
Learning ASR pathways: A sparse multilingual ASR model—0
Learning Attentive Meta-Transfer—0
A Comparison of Self-Supervised Pretraining Approaches for Predicting Disease Risk from Chest Radiograph Images—0
Learning bilingual word embeddings with (almost) no bilingual data—0
Learning Bound for Parameter Transfer Learning—0
Annotation Techniques for Judo Combat Phase Classification from Tournament Footage—0
Annotation-Free Cardiac Vessel Segmentation via Knowledge Transfer from Retinal Images—0
A Comparison of Methods for Neural Network Aggregation—0
Learning compact generalizable neural representations supporting perceptual grouping—0
Sign Language Recognition System using TensorFlow Object Detection API—0
Sign Language to Text Conversion in Real Time using Transfer Learning—0
An Iterative Transfer Learning Based Ensemble Technique for Automatic Short Answer Grading—0
Learning Cooperation and Online Planning Through Simulation and Graph Convolutional Network—0
An Iterative Similarity based Adaptation Technique for Cross-domain Text Classification—0
Learning Cross-lingual Representations for Event Coreference Resolution with Multi-view Alignment and Optimal Transport—0
SIGTYP 2020 Shared Task: Prediction of Typological Features—0
Learning Curves for Continual Learning in Neural Networks: Self-Knowledge Transfer and Forgetting—0
Silhouette: Toward Performance-Conscious and Transferable CPU Embeddings—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