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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3476–3500 of 10307 papers

TitleStatusHype
Graph Enabled Cross-Domain Knowledge Transfer—0
Computer-aided Diagnosis of Malaria through Transfer Learning using the ResNet50 Backbone—0
Classification of Skin Disease Using Transfer Learning in Convolutional Neural Networks—0
Improving automatic endoscopic stone recognition using a multi-view fusion approach enhanced with two-step transfer learning—0
Efficient Audio Captioning Transformer with Patchout and Text Guidance—0
Natural Language Robot Programming: NLP integrated with autonomous robotic grasping—0
Tag that issue: Applying API-domain labels in issue tracking systems—0
Source-free Domain Adaptation Requires Penalized Diversity—0
NUMSnet: Nested-U Multi-class Segmentation network for 3D Medical Image Stacks—0
Towards Efficient Task-Driven Model Reprogramming with Foundation Models—0
Multi-Domain Norm-referenced Encoding Enables Data Efficient Transfer Learning of Facial Expression Recognition—0
I2I: Initializing Adapters with Improvised KnowledgeCode1
Selective Knowledge Sharing for Privacy-Preserving Federated Distillation without A Good TeacherCode1
FisHook -- An Optimized Approach to Marine Specie Classification using MobileNetV2—0
Efficiently Aligned Cross-Lingual Transfer Learning for Conversational Tasks using Prompt-TuningCode0
GreekBART: The First Pretrained Greek Sequence-to-Sequence ModelCode0
Demonstration of a Standalone, Descriptive, and Predictive Digital Twin of a Floating Offshore Wind Turbine—0
Long-Tailed Visual Recognition via Self-Heterogeneous Integration with Knowledge ExcavationCode1
Adaptive Defective Area Identification in Material Surface Using Active Transfer Learning-based Level Set Estimation—0
Vision-Language Models for Vision Tasks: A SurveyCode4
Deep Manifold Learning for Reading Comprehension and Logical Reasoning Tasks with Polytuplet LossCode0
From Isolated Islands to Pangea: Unifying Semantic Space for Human Action Understanding—0
A comparison of small sample methods for Handshape RecognitionCode0
Leveraging medical Twitter to build a visual–language foundation model for pathology AICode2
HaLP: Hallucinating Latent Positives for Skeleton-based Self-Supervised Learning of ActionsCode0
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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