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 7926–7950 of 10307 papers

TitleStatusHype
Utilizing Deep Learning Towards Multi-modal Bio-sensing and Vision-based Affective Computing—0
Utilizing Grounded SAM for self-supervised frugal camouflaged human detection—0
Utilizing Mask R-CNN for Waterline Detection in Canoe Sprint Video Analysis—0
Utilizing Transfer Learning and a Customized Loss Function for Optic Disc Segmentation from Retinal Images—0
Utilizing Transfer Learning and pre-trained Models for Effective Forest Fire Detection: A Case Study of Uttarakhand—0
UZH_CLyp at SemEval-2023 Task 9: Head-First Fine-Tuning and ChatGPT Data Generation for Cross-Lingual Learning in Tweet Intimacy Prediction—0
VaCDA: Variational Contrastive Alignment-based Scalable Human Activity Recognition—0
Value Explicit Pretraining for Learning Transferable Representations—0
Variable Compliance Control for Robotic Peg-in-Hole Assembly: A Deep Reinforcement Learning Approach—0
Variable Extraction for Model Recovery in Scientific Literature—0
Variance-Covariance Regularization Improves Representation Learning—0
Variational Autoencoding Molecular Graphs with Denoising Diffusion Probabilistic Model—0
Variational Bayesian Adaptive Learning of Deep Latent Variables for Acoustic Knowledge Transfer—0
Variational Continual Bayesian Meta-Learning—0
Variational Information Distillation for Knowledge Transfer—0
Variational Quantum Kernels with Task-Specific Quantum Metric Learning—0
Variational Supervised Contrastive Learning—0
Variational Feature Disentangling for Fine-Grained Few-Shot Classification—0
Variational Transfer Learning using Cross-Domain Latent Modulation—0
Evaluation of a Low-Cost Single-Lead ECG Module for Vascular Ageing Prediction and Studying Smoking-induced Changes in ECG—0
VBSF-TLD: Validation-Based Approach for Soft Computing-Inspired Transfer Learning in Drone Detection—0
V-CAS: A Realtime Vehicle Anti Collision System Using Vision Transformer on Multi-Camera Streams—0
VCVTS: Multi-speaker Video-to-Speech synthesis via cross-modal knowledge transfer from voice conversion—0
Vector representations of text data in deep learning—0
Vehicle Detection of Multi-source Remote Sensing Data Using Active Fine-tuning Network—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