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 7051–7075 of 10307 papers

TitleStatusHype
Learning to diagnose cirrhosis from radiological and histological labels with joint self and weakly-supervised pretraining strategies—0
On the Generalization for Transfer Learning: An Information-Theoretic Analysis—0
Learning to Evaluate Translation Beyond English: BLEURT Submissions to the WMT Metrics 2020 Shared Task—0
Simple Semantic Annotation and Situation Frames: Two Approaches to Basic Text Understanding in LORELEI—0
Learning to Generate Textual Data—0
Learning to Harmonize Cross-vendor X-ray Images by Non-linear Image Dynamics Correction—0
Learning to Learn, from Transfer Learning to Domain Adaptation: A Unifying Perspective—0
Learning to Learn: How to Continuously Teach Humans and Machines—0
Learning to Learn: Meta-Critic Networks for Sample Efficient Learning—0
Learning to Learn Unlearned Feature for Brain Tumor Segmentation—0
Learning to Learn Weight Generation via Local Consistency Diffusion—0
An Inductive Transfer Learning Approach using Cycle-consistent Adversarial Domain Adaptation with Application to Brain Tumor Segmentation—0
Learning to Model the Tail—0
Learning to Profile: User Meta-Profile Network for Few-Shot Learning—0
A Comparative Study on Transfer Learning and Distance Metrics in Semantic Clustering over the COVID-19 Tweets—0
Learning to Progressively Recognize New Named Entities with Sequence to Sequence Models—0
Learning to Project for Cross-Task Knowledge Distillation—0
An Improvement for Capsule Networks using Depthwise Separable Convolution—0
Text-to-Speech for Under-Resourced Languages: Phoneme Mapping and Source Language Selection in Transfer Learning—0
Learning to Rank based on Analogical Reasoning—0
Learning to Rank Learning Curves—0
Beyond Fine-tuning: Few-Sample Sentence Embedding Transfer—0
Learning to Retain while Acquiring: Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation—0
Learning to rumble: Automated elephant call classification, detection and endpointing using deep architectures—0
Learning to search for and detect objects in foveal images using deep learning—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