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 6726–6750 of 10307 papers

TitleStatusHype
Self-Supervised Facial Representation Learning with Facial Region Awareness—0
Self-Supervised Human Activity Recognition with Localized Time-Frequency Contrastive Representation Learning—0
Self-Supervised In-Domain Representation Learning for Remote Sensing Image Scene Classification—0
Self-Supervised Interactive Object Segmentation Through a Singulation-and-Grasping Approach—0
Self-Supervised Intrinsic Image Decomposition—0
Self-Supervised Knowledge Transfer via Loosely Supervised Auxiliary Tasks—0
Self-supervised learning-based cervical cytology for the triage of HPV-positive women in resource-limited settings and low-data regime—0
Self-Supervised Learning Featuring Small-Scale Image Dataset for Treatable Retinal Diseases Classification—0
Biometric Fish Classification of Temperate Species Using Convolutional Neural Network with Squeeze-and-Excitation—0
Self-Supervised Learning for Medical Image Data with Anatomy-Oriented Imaging Planes—0
Self-Supervised Learning for Ordered Three-Dimensional Structures—0
Efficient Personalized Speech Enhancement through Self-Supervised Learning—0
Self-Supervised Learning for Pre-training Capsule Networks: Overcoming Medical Imaging Dataset Challenges—0
Self-Supervised Learning for Segmentation—0
BioNCERE: Non-Contrastive Enhancement For Relation Extraction In Biomedical Texts—0
Self-Supervised Learning of Video-Induced Visual Invariances—0
Self-Supervised Learning on 3D Point Clouds by Learning Discrete Generative Models—0
Self-supervised Learning with Geometric Constraints in Monocular Video: Connecting Flow, Depth, and Camera—0
BIOWISH: Biometric Recognition using Wearable Inertial Sensors detecting Heart Activity—0
Self-supervised Model Based on Masked Autoencoders Advance CT Scans Classification—0
Self-supervised Pretraining and Transfer Learning Enable Flu and COVID-19 Predictions in Small Mobile Sensing Datasets—0
Self-Supervised Pre-Training for Precipitation Post-Processor—0
BIRD: Behavior Induction via Representation-structure Distillation—0
Self-Supervised Pretraining on Paired Sequences of fMRI Data for Transfer Learning to Brain Decoding Tasks—0
Self-Supervised Representation Learning From Multi-Domain Data—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