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 3851–3875 of 10307 papers

TitleStatusHype
Extremely Simple Out-of-distribution Detection for Audio-visual Generalized Zero-shot Learning—0
A Robust Ensemble Model for Patasitic Egg Detection and Classification—0
Eye Disease Classification Using Deep Learning Techniques—0
FabKG: A Knowledge graph of Manufacturing Science domain utilizing structured and unconventional unstructured knowledge source—0
A Multimodal German Dataset for Automatic Lip Reading Systems and Transfer Learning—0
Generalized 3D Self-supervised Learning Framework via Prompted Foreground-Aware Feature Contrast—0
Face2Text revisited: Improved data set and baseline results—0
FaceLeaks: Inference Attacks against Transfer Learning Models via Black-box Queries—0
Face Mask Detection using Transfer Learning of InceptionV3—0
FISTNet: FusIon of STyle-path generative Networks for Facial Style Transfer—0
Faces of Experimental Pain: Transferability of Deep Learned Heat Pain Features to Electrical Pain—0
Facial Action Unit Recognition Based on Transfer Learning—0
Facial Anatomical Landmark Detection using Regularized Transfer Learning with Application to Fetal Alcohol Syndrome Recognition—0
Combining Behaviors with the Successor Features Keyboard—0
Combining Convolution and Recursive Neural Networks for Sentiment Analysis—0
Using LLMs to Establish Implicit User Sentiment of Software Desirability—0
Decomposition-Based Transfer Distance Metric Learning for Image Classification—0
Facial Landmark Correlation Analysis—0
Combining Deep Transfer Learning with Signal-image Encoding for Multi-Modal Mental Wellbeing Classification—0
Facilitating Reinforcement Learning for Process Control Using Transfer Learning: Overview and Perspectives—0
A Multi-media Approach to Cross-lingual Entity Knowledge Transfer—0
Decomposed Cross-modal Distillation for RGB-based Temporal Action Detection—0
Decomposable Probability-of-Success Metrics in Algorithmic Search—0
Automated Segmentation and Analysis of Microscopy Images of Laser Powder Bed Fusion Melt Tracks—0
Adaptive Transfer Learning for Plant Phenotyping—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