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 4351–4375 of 10307 papers

TitleStatusHype
CDS: Cross-Domain Self-Supervised Pre-Training—0
Embeddings models for Buddhist Sanskrit—0
Embedding Semantic Similarity in Tree Kernels for Domain Adaptation of Relation Extraction—0
CDR-Adapter: Learning Adapters to Dig Out More Transferring Ability for Cross-Domain Recommendation Models—0
Embedding Compression for Teacher-to-Student Knowledge Transfer—0
CDKT-FL: Cross-Device Knowledge Transfer using Proxy Dataset in Federated Learning—0
CDCGen: Cross-Domain Conditional Generation via Normalizing Flows and Adversarial Training—0
Embedded Knowledge Distillation in Depth-Level Dynamic Neural Network—0
CCT-Net: Category-Invariant Cross-Domain Transfer for Medical Single-to-Multiple Disease Diagnosis—0
ELSIM: End-to-end learning of reusable skills through intrinsic motivation—0
CCS-GAN: COVID-19 CT-scan classification with very few positive training images—0
ELiTe: Efficient Image-to-LiDAR Knowledge Transfer for Semantic Segmentation—0
ELISA-EDL: A Cross-lingual Entity Extraction, Linking and Localization System—0
CB-HVTNet: A channel-boosted hybrid vision transformer network for lymphocyte assessment in histopathological images—0
An X3D Neural Network Analysis for Runner's Performance Assessment in a Wild Sporting Environment—0
Adversarial Feature Training for Generalizable Robotic Visuomotor Control—0
Adversarial Domain Adaptation Being Aware of Class Relationships—0
Electron-nucleus cross sections from transfer learning—0
ELCC: the Emergent Language Corpus Collection—0
Elastic Information Bottleneck—0
Cause-Effect Preservation and Classification using Neurochaos Learning—0
Causal Transfer for Imitation Learning and Decision Making under Sensor-shift—0
ET-GAN: Cross-Language Emotion Transfer Based on Cycle-Consistent Generative Adversarial Networks—0
Causal Time-Series Synchronization for Multi-Dimensional Forecasting—0
An Unsupervised Multiple-Task and Multiple-Teacher Model for Cross-lingual Named Entity Recognition—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