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 3576–3600 of 10307 papers

TitleStatusHype
AdPE: Adversarial Positional Embeddings for Pretraining Vision Transformers via MAE+Code0
Transfer Learning for Real-time Deployment of a Screening Tool for Depression Detection Using Actigraphy—0
A Contrastive Knowledge Transfer Framework for Model Compression and Transfer LearningCode0
Relational Multi-Task Learning: Modeling Relations between Data and TasksCode3
Statistical Hardware Design With Multi-model Active Learning—0
Revisit Parameter-Efficient Transfer Learning: A Two-Stage Paradigm—0
Few-Shot Classification of Autism Spectrum Disorder using Site-Agnostic Meta-Learning and Brain MRI—0
Good Neighbors Are All You Need for Chinese Grapheme-to-Phoneme Conversion—0
AutoTransfer: AutoML with Knowledge Transfer -- An Application to Graph Neural NetworksCode0
DAA: A Delta Age AdaIN operation for age estimation via binary code transformerCode1
ICICLE: Interpretable Class Incremental Continual LearningCode0
Multi-class Skin Cancer Classification Architecture Based on Deep Convolutional Neural Network—0
One-Shot Segmentation of Novel White Matter Tracts via Extensive Data AugmentationCode0
Traffic Prediction with Transfer Learning: A Mutual Information-based Approach—0
Self-supervised learning-based general laboratory progress pretrained model for cardiovascular event detection—0
Transformer-based approaches to Sentiment Detection—0
Transferable Deep Learning Power System Short-Term Voltage Stability Assessment with Physics-Informed Topological Feature Engineering—0
Improving physics-informed neural networks with meta-learned optimization—0
Functional Knowledge Transfer with Self-supervised Representation LearningCode0
Generalized 3D Self-supervised Learning Framework via Prompted Foreground-Aware Feature Contrast—0
Software Vulnerability Prediction Knowledge Transferring Between Programming Languages—0
Overwriting Pretrained Bias with Finetuning DataCode0
Optimizing Federated Learning for Medical Image Classification on Distributed Non-iid Datasets with Partial Labels—0
Scaling Up 3D Kernels with Bayesian Frequency Re-parameterization for Medical Image SegmentationCode1
HiNet: Novel Multi-Scenario & Multi-Task Learning with Hierarchical Information ExtractionCode1
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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