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 2101–2125 of 10307 papers

TitleStatusHype
OpenMEDLab: An Open-source Platform for Multi-modality Foundation Models in Medicine—0
Challenges in Pre-Training Graph Neural Networks for Context-Based Fake News Detection: An Evaluation of Current Strategies and Resource LimitationsCode0
Deep Neural Network Models Trained With A Fixed Random Classifier Transfer Better Across Domains—0
Automated Testing of Spatially-Dependent Environmental Hypotheses through Active Transfer Learning—0
Exploration of Adapter for Noise Robust Automatic Speech Recognition—0
Emotion Classification in Low and Moderate Resource Languages—0
Diffusion-Based Neural Network Weights GenerationCode1
Investigating Continual Pretraining in Large Language Models: Insights and Implications—0
Beyond the Known: Investigating LLMs Performance on Out-of-Domain Intent Detection—0
NocPlace: Nocturnal Visual Place Recognition via Generative and Inherited Knowledge TransferCode1
Transfer Learning Bayesian Optimization to Design Competitor DNA Molecules for Use in Diagnostic AssaysCode0
MedContext: Learning Contextual Cues for Efficient Volumetric Medical SegmentationCode1
Intensive Care as One Big Sequence Modeling ProblemCode0
DTCM: Deep Transformer Capsule Mutual Distillation for Multivariate Time Series Classification—0
CARTE: Pretraining and Transfer for Tabular LearningCode2
Enhancing Continuous Domain Adaptation with Multi-Path Transfer Curriculum—0
CLAP: Learning Transferable Binary Code Representations with Natural Language SupervisionCode2
Few-Shot Learning for Annotation-Efficient Nucleus Instance Segmentation—0
GenAINet: Enabling Wireless Collective Intelligence via Knowledge Transfer and Reasoning—0
Exploring the Power of Pure Attention Mechanisms in Blind Room Parameter Estimation—0
VOLoc: Visual Place Recognition by Querying Compressed Lidar MapCode2
Adversarial-Robust Transfer Learning for Medical Imaging via Domain Assimilation—0
Task Specific Pretraining with Noisy Labels for Remote Sensing Image Segmentation—0
StochCA: A Novel Approach for Exploiting Pretrained Models with Cross-AttentionCode0
Emotion Classification in Short English Texts using Deep Learning Techniques—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