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 6751–6775 of 10307 papers

TitleStatusHype
Self-Supervised Representation Learning from Temporal Ordering of Automated Driving Sequences—0
Self-supervised representations in speech-based depression detection—0
Self-Supervised RF Signal Representation Learning for NextG Signal Classification with Deep Learning—0
Self-supervised similarity models based on well-logging data—0
Self-Supervised Transfer Learning for Hand Mesh Recovery From Binocular Images—0
Self-supervised Transformer for Deepfake Detection—0
Self-supervised video pretraining yields robust and more human-aligned visual representations—0
Self-supervised Visual Attribute Learning for Fashion Compatibility—0
Self-supervised visual learning for analyzing firearms trafficking activities on the Web—0
Self-supervised visual learning in the low-data regime: a comparative evaluation—0
Self-Transfer Learning for Fully Weakly Supervised Object Localization—0
Self-transfer learning via patches: A prostate cancer triage approach based on bi-parametric MRI—0
Semantically Proportional Patchmix for Few-Shot Learning—0
Semantic and Visual Similarities for Efficient Knowledge Transfer in CNN Training—0
Semantic-Aware Generative Adversarial Nets for Unsupervised Domain Adaptation in Chest X-ray Segmentation—0
Bit Cipher -- A Simple yet Powerful Word Representation System that Integrates Efficiently with Language Models—0
Semantic decoupled representation learning for remote sensing image change detection—0
Semantic-Discriminative Mixup for Generalizable Sensor-based Cross-domain Activity Recognition—0
Semantic-diversity transfer network for generalized zero-shot learning via inner disagreement based OOD detector—0
Semantic Graph for Zero-Shot Learning—0
Semantic-guided Cross-Modal Prompt Learning for Skeleton-based Zero-shot Action Recognition—0
Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Tasks—0
Semantic Parsing in Limited Resource Conditions—0
Semantic Pose using Deep Networks Trained on Synthetic RGB-D—0
Semantic Positive Pairs for Enhancing Visual Representation Learning of Instance Discrimination methods—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