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 9801–9850 of 10307 papers

TitleStatusHype
Evaluating Knowledge Transfer in Neural Network for Medical Images—0
Evaluating Pixel Language Models on Non-Standardized Languages—0
Evaluating Query Efficiency and Accuracy of Transfer Learning-based Model Extraction Attack in Federated Learning—0
Evaluating Standard and Dialectal Frisian ASR: Multilingual Fine-tuning and Language Identification for Improved Low-resource Performance—0
Evaluating the Cross-Lingual Effectiveness of Massively Multilingual Neural Machine Translation—0
Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing—0
Evaluating the Performance of StyleGAN2-ADA on Medical Images—0
Evaluating the structure of cognitive tasks with transfer learning—0
Evaluating the Transferability and Adversarial Discrimination of Convolutional Neural Networks for Threat Object Detection and Classification within X-Ray Security Imagery—0
Evaluating Transferability for Covid 3D Localization Using CT SARS-CoV-2 segmentation models—0
Evaluating Transferability in Retrieval Tasks: An Approach Using MMD and Kernel Methods—0
Transfer Learning for Wildlife Classification: Evaluating YOLOv8 against DenseNet, ResNet, and VGGNet on a Custom Dataset—0
Evaluating Unsupervised Representation Learning for Detecting Stances of Fake News—0
Evaluating zero-shot transfers and multilingual models for dependency parsing and POS tagging within the low-resource language family Tupían—0
Evaluation and Comparison of Emotionally Evocative Image Augmentation Methods—0
Evaluation of Deep Learning based Pose Estimation for Sign Language Recognition—0
Evaluation of taxonomic and neural embedding methods for calculating semantic similarity—0
Evaluation of Transfer Learning and Domain Adaptation for Analyzing German-Speaking Job Advertisements—0
Evaluation of Transfer Learning for Classification of: (1) Diabetic Retinopathy by Digital Fundus Photography and (2) Diabetic Macular Edema, Choroidal Neovascularization and Drusen by Optical Coherence Tomography—0
Evaluation of Transfer Learning for Adverse Drug Event (ADE) and Medication Entity Extraction—0
Evaluation of Transfer Learning for Polish with a text-to-text model—0
Evaluation of Transfer Learning for Polish with a Text-to-Text Model—0
Evaluation-oriented Knowledge Distillation for Deep Face Recognition—0
Event-based Vision meets Deep Learning on Steering Prediction for Self-driving Cars—0
Event Camera Data Dense Pre-training—0
Event Camera Data Pre-training—0
EventDance: Unsupervised Source-free Cross-modal Adaptation for Event-based Object Recognition—0
Event Extraction in Basque: Typologically motivated Cross-Lingual Transfer-Learning Analysis—0
Event USKT : U-State Space Model in Knowledge Transfer for Event Cameras—0
Everything is a Video: Unifying Modalities through Next-Frame Prediction—0
Everything old is new again: A multi-view learning approach to learning using privileged information and distillation—0
Evidence-empowered Transfer Learning for Alzheimer's Disease—0
Evolutionary Algorithms Approach For Search Based On Semantic Document Similarity—0
Evolutionary Algorithms in the Light of SGD: Limit Equivalence, Minima Flatness, and Transfer Learning—0
Evolutionary Dynamic Multi-objective Optimization Via Regression Transfer Learning—0
Evolutionary Gait Transfer of Multi-Legged Robots in Complex Terrains—0
Evolutionary Multitasking with Solution Space Cutting for Point Cloud Registration—0
Evolution of ReID: From Early Methods to LLM Integration—0
Evolution of transfer learning in natural language processing—0
Evolving Image Compositions for Feature Representation Learning—0
EvoSampling: A Granular Ball-based Evolutionary Hybrid Sampling with Knowledge Transfer for Imbalanced Learning—0
Examining the behaviour of state-of-the-art convolutional neural networks for brain tumor detection with and without transfer learning—0
EXIT: An EXplicit Interest Transfer Framework for Cross-Domain Recommendation—0
Exo2EgoDVC: Dense Video Captioning of Egocentric Procedural Activities Using Web Instructional Videos—0
ExoMiner++: Enhanced Transit Classification and a New Vetting Catalog for 2-Minute TESS Data—0
Expanding Deep Learning-based Sensing Systems with Multi-Source Knowledge Transfer—0
Expanding Expressiveness of Diffusion Models with Limited Data via Self-Distillation based Fine-Tuning—0
ExpandNets: Linear Over-parameterization to Train Compact Convolutional Networks—0
Expectation maximization transfer learning and its application for bionic hand prostheses—0
Experience Reuse with Probabilistic Movement Primitives—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