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 2051–2100 of 10307 papers

TitleStatusHype
AnyTouch: Learning Unified Static-Dynamic Representation across Multiple Visuo-tactile Sensors—0
CellLineNet: End-to-End Learning and Transfer Learning For Multiclass Epithelial Breast cell Line Classification via a Convolutional Neural Network—0
Adversarial Imitation via Variational Inverse Reinforcement Learning—0
CSECU-DSG at WNUT-2020 Task 2: Exploiting Ensemble of Transfer Learning and Hand-crafted Features for Identification of Informative COVID-19 English Tweets—0
CellCentroidFormer: Combining Self-attention and Convolution for Cell Detection—0
Deep Feature Learning from a Hospital-Scale Chest X-ray Dataset with Application to TB Detection on a Small-Scale Dataset—0
Any-Shot Sequential Anomaly Detection in Surveillance Videos—0
CEHR-BERT: Incorporating temporal information from structured EHR data to improve prediction tasks—0
Adversarial Fine-tune with Dynamically Regulated Adversary—0
CrystalGPT: Enhancing system-to-system transferability in crystallization prediction and control using time-series-transformers—0
CSG0: Continual Urban Scene Generation with Zero Forgetting—0
CDS: Cross-Domain Self-Supervised Pre-Training—0
CDR-Adapter: Learning Adapters to Dig Out More Transferring Ability for Cross-Domain Recommendation Models—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
Adversarial Feature Training for Generalizable Robotic Visuomotor Control—0
CCT-Net: Category-Invariant Cross-Domain Transfer for Medical Single-to-Multiple Disease Diagnosis—0
CCS-GAN: COVID-19 CT-scan classification with very few positive training images—0
An X3D Neural Network Analysis for Runner's Performance Assessment in a Wild Sporting Environment—0
CB-HVTNet: A channel-boosted hybrid vision transformer network for lymphocyte assessment in histopathological images—0
Adversarial Domain Adaptation Being Aware of Class Relationships—0
Bias and Generalizability of Foundation Models across Datasets in Breast Mammography—0
Crude Oil-related Events Extraction and Processing: A Transfer Learning Approach—0
CST: Calibration Side-Tuning for Parameter and Memory Efficient Transfer Learning—0
Cause-Effect Preservation and Classification using Neurochaos Learning—0
An Unsupervised Multiple-Task and Multiple-Teacher Model for Cross-lingual Named Entity Recognition—0
Causal Transfer for Imitation Learning and Decision Making under Sensor-shift—0
Causal Time-Series Synchronization for Multi-Dimensional Forecasting—0
Action Learning for 3D Point Cloud Based Organ Segmentation—0
Actionable Phrase Detection using NLP—0
An Unified Search and Recommendation Foundation Model for Cold-Start Scenario—0
Causal Reinforcement Learning: A Survey—0
Adversarial Contrastive Distillation with Adaptive Denoising—0
Causal Modeling in Multi-Context Systems: Distinguishing Multiple Context-Specific Causal Graphs which Account for Observational Support—0
Causally Regularized Learning with Agnostic Data Selection Bias—0
Crowd-Powered Data Mining—0
Crowd Sourcing based Active Learning Approach for Parking Sign Recognition—0
An Uncertainty-Aware Deep Learning Framework for Defect Detection in Casting Products—0
Transfer Learning for Individual Treatment Effect Estimation—0
Advantages of biologically-inspired adaptive neural activation in RNNs during learning—0
Anti-Spoofing Using Transfer Learning with Variational Information Bottleneck—0
Causality in Neural Networks -- An Extended Abstract—0
Adversarial attacks on hybrid classical-quantum Deep Learning models for Histopathological Cancer Detection—0
CrossVoice: Crosslingual Prosody Preserving Cascade-S2ST using Transfer Learning—0
Causal-Invariant Cross-Domain Out-of-Distribution Recommendation—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
Causal Inference based Transfer Learning with LLMs: An Efficient Framework for Industrial RUL Prediction—0
Causal Discovery Inspired Unsupervised Domain Adaptation for Emotion-Cause Pair Extraction—0
A novel Trunk Branch-net PINN for flow and heat transfer prediction in porous medium—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