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 4351–4400 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
Causal Reinforcement Learning: A Survey—0
Efficient Transfer Learning via Joint Adaptation of Network Architecture and Weight—0
Efficient Transfer Learning Schemes for Personalized Language Modeling using Recurrent Neural Network—0
Causal Modeling in Multi-Context Systems: Distinguishing Multiple Context-Specific Causal Graphs which Account for Observational Support—0
Efficient Transfer Learning in Diffusion Models via Adversarial Noise—0
Efficient Transfer Learning Framework for Cross-Domain Click-Through Rate Prediction—0
Causally Regularized Learning with Agnostic Data Selection Bias—0
An Unified Search and Recommendation Foundation Model for Cold-Start Scenario—0
Adversarial Contrastive Distillation with Adaptive Denoising—0
Efficient Transfer Learning for Quality Estimation with Bottleneck Adapter Layer—0
Efficient transfer learning and online adaptation with latent variable models for continuous control—0
Transfer Learning for Individual Treatment Effect Estimation—0
An Uncertainty-Aware Deep Learning Framework for Defect Detection in Casting Products—0
Efficient Training of Deep Convolutional Neural Networks by Augmentation in Embedding Space—0
Efficient textual explanations for complex road and traffic scenarios based on semantic segmentation—0
Efficient Task Transfer for HLS DSE—0
Causality in Neural Networks -- An Extended Abstract—0
Anti-Spoofing Using Transfer Learning with Variational Information Bottleneck—0
Adversarial attacks on hybrid classical-quantum Deep Learning models for Histopathological Cancer Detection—0
Causal-Invariant Cross-Domain Out-of-Distribution Recommendation—0
Efficient PINNs: Multi-Head Unimodular Regularization of the Solutions Space—0
Convex Hull Prediction for Adaptive Video Streaming by Recurrent Learning—0
Causal Inference from Small High-dimensional Datasets—0
A Novel TSK Fuzzy System Incorporating Multi-view Collaborative Transfer Learning for Personalized Epileptic EEG Detection—0
Efficient Pairwise Learning Using Kernel Ridge Regression: an Exact Two-Step Method—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