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 5826–5850 of 10307 papers

TitleStatusHype
Action Learning for 3D Point Cloud Based Organ Segmentation—0
Generate labeled training data using Prompt Programming and GPT-3. An example of Big Five Personality Classification—0
Generating Abstractive Summaries with Finetuned Language Models—0
Generating an interactive online map of future sea level rise along the North Shore of Vancouver: methods and insights on enabling geovisualisation for coastal communities—0
Segmentation Framework for Heat Loss Identification in Thermal Images: Empowering Scottish Retrofitting and Thermographic Survey Companies—0
Generating Realistic COVID19 X-rays with a Mean Teacher + Transfer Learning GAN—0
Generating Stylistically Consistent Dialog Responses with Transfer Learning—0
Generating Synthetic Datasets by Interpolating along Generalized Geodesics—0
Generating Synthetic Datasets for Few-shot Prompt Tuning—0
Generating Synthetic Stereo Datasets using 3D Gaussian Splatting and Expert Knowledge Transfer—0
Generating Table Vector Representations—0
Segmentation of Mental Foramen in Orthopantomographs: A Deep Learning Approach—0
Generation-Distillation for Efficient Natural Language Understanding in Low-Data Settings—0
Generation of Realistic Cloud Access Times for Mobile Application Testing using Transfer Learning—0
Generation of synthetic data using breast cancer dataset and classification with resnet18—0
Generation of Uncorrelated Residual Variables for Chemical Process Fault Diagnosis via Transfer Learning-based Input-Output Decoupled Network—0
Generative Adversarial Data Programming—0
Generative Adversarial Imitation Learning for Empathy-based AI—0
Generative Adversarial Networks for Annotated Data Augmentation in Data Sparse NLU—0
Generative Adversarial Networks For Data Scarcity Industrial Positron Images With Attention—0
Segmentation of Shoulder Muscle MRI Using a New Region and Edge based Deep Auto-Encoder—0
Segmenting across places: The need for fair transfer learning with satellite imagery—0
Generative Distribution Prediction: A Unified Approach to Multimodal Learning—0
A study on Deep Convolutional Neural Networks, Transfer Learning and Ensemble Model for Breast Cancer Detection—0
Generative Image Translation for Data Augmentation of Bone Lesion Pathology—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