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 4151–4175 of 10307 papers

TitleStatusHype
Enhancing Trust in LLMs: Algorithms for Comparing and Interpreting LLMs—0
Classification of Colorectal Cancer Polyps via Transfer Learning and Vision-Based Tactile Sensing—0
Enhancing Translation for Indigenous Languages: Experiments with Multilingual Models—0
Classification of Chest Diseases using Wavelet Transforms and Transfer Learning—0
Enhancing Transfer Learning with Flexible Nonparametric Posterior Sampling—0
Enhancing Transfer Learning for Medical Image Classification with SMOTE: A Comparative Study—0
Enhancing the efficiency of protein language models with minimal wet-lab data through few-shot learning—0
Classification of Breast Cancer Lesions in Ultrasound Images by using Attention Layer and loss Ensembles in Deep Convolutional Neural Networks—0
Approaching Neural Chinese Word Segmentation as a Low-Resource Machine Translation Task—0
Adversarial Training Helps Transfer Learning via Better Representations—0
Enhancing the Authenticity of Rendered Portraits with Identity-Consistent Transfer Learning—0
Classification of breast cancer histology images using transfer learning—0
Enhancing team performance with transfer-learning during real-world human-robot collaboration—0
Approaches for enhancing extrapolability in process-based and data-driven models in hydrology—0
Enhancing Skin Disease Classification Leveraging Transformer-based Deep Learning Architectures and Explainable AI—0
Classification of All Blood Cell Images using ML and DL Models—0
Enhancing radioisotope identification in gamma spectra via supervised domain adaptation—0
Enhancing Prototypical Few-Shot Learning by Leveraging the Local-Level Strategy—0
Classification of Beer Bottles using Object Detection and Transfer Learning—0
Applying Transfer Learning To Deep Learned Models For EEG Analysis—0
Adversarial Training Based Multi-Source Unsupervised Domain Adaptation for Sentiment Analysis—0
Enhancing Pothole Detection and Characterization: Integrated Segmentation and Depth Estimation in Road Anomaly Systems—0
Enhancing Polynomial Chaos Expansion Based Surrogate Modeling using a Novel Probabilistic Transfer Learning Strategy—0
Classification Of Automotive Targets Using Inverse Synthetic Aperture Radar Images—0
Enhancing Performance, Calibration Time and Efficiency in Brain-Machine Interfaces through Transfer Learning and Wearable EEG Technology—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