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 9351–9400 of 10307 papers

TitleStatusHype
DiCoRe: Enhancing Zero-shot Event Detection via Divergent-Convergent LLM Reasoning—0
Dictionary Learning Under Generative Coefficient Priors with Applications to Compression—0
Differentiable Architecture Pruning for Transfer Learning—0
Differentiable Multi-Fidelity Fusion: Efficient Learning of Physics Simulations with Neural Architecture Search and Transfer Learning—0
Differentially Private Meta-Learning—0
Differentially Private Transferrable Deep Learning with Membership-Mappings—0
Differentially Private Video Activity Recognition—0
Differential Private Federated Transfer Learning for Mental Health Monitoring in Everyday Settings: A Case Study on Stress Detection—0
Differential Private Knowledge Transfer for Privacy-Preserving Cross-Domain Recommendation—0
Differential Private Stack Generalization with an Application to Diabetes Prediction—0
Difficulty Estimation and Simplification of French Text Using LLMs—0
Diffusion Augmented Agents: A Framework for Efficient Exploration and Transfer Learning—0
Diffusion-based Human Motion Style Transfer with Semantic Guidance—0
Diffusion Tuning: Transferring Diffusion Models via Chain of Forgetting—0
DiFuse-Net: RGB and Dual-Pixel Depth Estimation using Window Bi-directional Parallax Attention and Cross-modal Transfer Learning—0
DIGEST: Deeply supervIsed knowledGE tranSfer neTwork learning for brain tumor segmentation with incomplete multi-modal MRI scans—0
Digging Deeper into Egocentric Gaze Prediction—0
Digital Fingerprinting of Microstructures—0
Digital Twin Synchronization: Bridging the Sim-RL Agent to a Real-Time Robotic Additive Manufacturing Control—0
Digital Twin Framework for Optimal and Autonomous Decision-Making in Cyber-Physical Systems: Enhancing Reliability and Adaptability in the Oil and Gas Industry—0
DiHuR: Diffusion-Guided Generalizable Human Reconstruction—0
Dimensionality Expansion of Load Monitoring Time Series and Transfer Learning for EMS—0
Direct mineral content prediction from drill core images via transfer learning—0
Direct Network Transfer: Transfer Learning of Sentence Embeddings for Semantic Similarity—0
Disaggregating Hops: Can We Guide a Multi-Hop Reasoning Language Model to Incrementally Learn at each Hop?—0
Discovering Hidden Physics Behind Transport Dynamics—0
Discover Your Social Identity from What You Tweet: a Content Based Approach—0
Discriminative Label Consistent Domain Adaptation—0
Discriminative Partial Domain Adversarial Network—0
Discriminative Pattern Calibration Mechanism for Source-Free Domain Adaptation—0
Discriminative Transfer Learning for General Image Restoration—0
Discriminative Transfer Learning with Tree-based Priors—0
Discriminatory Transfer—0
Disease Classification and Impact of Pretrained Deep Convolution Neural Networks on Diverse Medical Imaging Datasets across Imaging Modalities—0
Disengagement Cause-and-Effect Relationships Extraction Using an NLP Pipeline—0
Disentangled Adversarial Autoencoder for Subject-Invariant Physiological Feature Extraction—0
Disentangled Adversarial Transfer Learning for Physiological Biosignals—0
Disentangled Cumulants Help Successor Representations Transfer to New Tasks—0
Disentangled State Space Representations—0
Disentangled World Models: Learning to Transfer Semantic Knowledge from Distracting Videos for Reinforcement Learning—0
Disentangling coincident cell events using deep transfer learning and compressive sensing—0
Disentangling Quantum and Classical Contributions in Hybrid Quantum Machine Learning Architectures—0
Disentangling Space and Time in Video with Hierarchical Variational Auto-encoders—0
Disentangling the Effects of Data Augmentation and Format Transform in Self-Supervised Learning of Image Representations—0
Disentangling the Roles of Target-Side Transfer and Regularization in Multilingual Machine Translation—0
Disentangling Transfer and Interference in Multi-Domain Learning—0
Disjoint Label Space Transfer Learning with Common Factorised Space—0
Disposable Transfer Learning for Selective Source Task Unlearning—0
Distant Domain Transfer Learning for Medical Imaging—0
Distant Transfer Learning via Deep Random Walk—0
Show:102550
← PrevPage 188 of 207Next →
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