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 9501–9550 of 10307 papers

TitleStatusHype
Do sound event representations generalize to other audio tasks? A case study in audio transfer learning—0
Do the Frankenstein, or how to achieve better out-of-distribution performance with manifold mixing model soup—0
Double-Dip: Thwarting Label-Only Membership Inference Attacks with Transfer Learning and Randomization—0
Double Double Descent: On Generalization Errors in Transfer Learning between Linear Regression Tasks—0
DoubleField: Bridging the Neural Surface and Radiance Fields for High-fidelity Human Reconstruction and Rendering—0
DoubleTransfer at MEDIQA 2019: Multi-Source Transfer Learning for Natural Language Understanding in the Medical Domain—0
Double Transfer Learning for Breast Cancer Histopathologic Image Classification—0
Do We Really Need a Large Number of Visual Prompts?—0
DRAFT: A Novel Framework to Reduce Domain Shifting in Self-supervised Learning and Its Application to Children's ASR—0
DRDr: Automatic Masking of Exudates and Microaneurysms Caused By Diabetic Retinopathy Using Mask R-CNN and Transfer Learning—0
DRDr II: Detecting the Severity Level of Diabetic Retinopathy Using Mask RCNN and Transfer Learning—0
DRDrV3: Complete Lesion Detection in Fundus Images Using Mask R-CNN, Transfer Learning, and LSTM—0
DreamBeast: Distilling 3D Fantastical Animals with Part-Aware Knowledge Transfer—0
DRG-Net: Interactive Joint Learning of Multi-lesion Segmentation and Classification for Diabetic Retinopathy Grading—0
Driver Drowsiness Estimation from EEG Signals Using Online Weighted Adaptation Regularization for Regression (OwARR)—0
Driving Experience Transfer Method for End-to-End Control of Self-Driving Cars—0
Driving Tasks Transfer in Deep Reinforcement Learning for Decision-making of Autonomous Vehicles—0
Dropping Networks for Transfer Learning—0
Drowsiness Detection Based On Driver Temporal Behavior Using a New Developed Dataset—0
DRP: Distilled Reasoning Pruning with Skill-aware Step Decomposition for Efficient Large Reasoning Models—0
Drug repositioning for Alzheimer's disease with transfer learning—0
DS4DH at TREC Health Misinformation 2021: Multi-Dimensional Ranking Models with Transfer Learning and Rank Fusion—0
DTCM: Deep Transformer Capsule Mutual Distillation for Multivariate Time Series Classification—0
DT-LET: Deep Transfer Learning by Exploring where to Transfer—0
DuA: Dual Attentive Transformer in Long-Term Continuous EEG Emotion Analysis—0
Dual Adversarial Semantics-Consistent Network for Generalized Zero-Shot Learning—0
Dual-Agent GANs for Photorealistic and Identity Preserving Profile Face Synthesis—0
Dual Branch Deep Learning Network for Detection and Stage Grading of Diabetic Retinopathy—0
Dual-Correction Adaptation Network for Noisy Knowledge Transfer—0
Dual Decomposition of Weights and Singular Value Low Rank Adaptation—0
Dual Memory Architectures for Fast Deep Learning of Stream Data via an Online-Incremental-Transfer Strategy—0
Dual Path Structural Contrastive Embeddings for Learning Novel Objects—0
Learning to Prompt Your Domain for Vision-Language Models—0
Dual Prototyping with Domain and Class Prototypes for Affective Brain-Computer Interface in Unseen Target Conditions—0
Dual Relation Mining Network for Zero-Shot Learning—0
Dual Scale-aware Adaptive Masked Knowledge Distillation for Object Detection—0
Dual-State Personalized Knowledge Tracing with Emotional Incorporation—0
Dual-stream contrastive predictive network with joint handcrafted feature view for SAR ship classification—0
Dual-Teacher Class-Incremental Learning With Data-Free Generative Replay—0
Durocmien: A deep framework for duroc skeleton extraction in constraint environment—0
DVS: Blood cancer detection using novel CNN-based ensemble approach—0
DWMD: Dimensional Weighted Orderwise Moment Discrepancy for Domain-specific Hidden Representation Matching—0
Dyn-Adapter: Towards Disentangled Representation for Efficient Visual Recognition—0
Dynamically Composing Domain-Data Selection with Clean-Data Selection by "Co-Curricular Learning" for Neural Machine Translation—0
Dynamically Composing Domain-Data Selection with Clean-Data Selection by ``Co-Curricular Learning'' for Neural Machine Translation—0
Dynamically enhanced static handwriting representation for Parkinson's disease detection—0
Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning—0
Dynamically writing coupled memories using a reinforcement learning agent, meeting physical bounds—0
Dynamic Corrective Self-Distillation for Better Fine-Tuning of Pretrained Models—0
Dynamic Ensemble Reasoning for LLM Experts—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