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 2126–2150 of 10307 papers

TitleStatusHype
MMW-Carry: Enhancing Carry Object Detection through Millimeter-Wave Radar-Camera Fusion—0
Artificial Bee Colony optimization of Deep Convolutional Neural Networks in the context of Biomedical Imaging—0
Substrate Prediction for RiPP Biosynthetic Enzymes via Masked Language Modeling and Transfer LearningCode0
PEMT: Multi-Task Correlation Guided Mixture-of-Experts Enables Parameter-Efficient Transfer Learning—0
Which Model to Transfer? A Survey on Transferability Estimation—0
Practical Insights into Knowledge Distillation for Pre-Trained Models—0
Smoothness Adaptive Hypothesis Transfer Learning—0
CLCE: An Approach to Refining Cross-Entropy and Contrastive Learning for Optimized Learning Fusion—0
TIE-KD: Teacher-Independent and Explainable Knowledge Distillation for Monocular Depth EstimationCode0
Global Safe Sequential Learning via Efficient Knowledge TransferCode0
SHM-Traffic: DRL and Transfer learning based UAV Control for Structural Health Monitoring of Bridges with Traffic—0
ARL2: Aligning Retrievers for Black-box Large Language Models via Self-guided Adaptive Relevance LabelingCode0
Simple and Effective Transfer Learning for Neuro-Symbolic Integration—0
Wisdom of Committee: Distilling from Foundation Model to Specialized Application Model—0
LinkSAGE: Optimizing Job Matching Using Graph Neural Networks—0
Scalable and reliable deep transfer learning for intelligent fault detection via multi-scale neural processes embedded with knowledge—0
Indiscriminate Data Poisoning Attacks on Pre-trained Feature Extractors—0
Learning Causal Domain-Invariant Temporal Dynamics for Few-Shot Action Recognition—0
CST: Calibration Side-Tuning for Parameter and Memory Efficient Transfer Learning—0
Cross-Domain Transfer Learning with CoRTe: Consistent and Reliable Transfer from Black-Box to Lightweight Segmentation Model—0
Induced Model Matching: How Restricted Models Can Help Larger OnesCode0
Predicting trucking accidents with truck drivers 'safety climate perception across companies: A transfer learning approach—0
Molecule Generation and Optimization for Efficient Fragrance CreationCode0
Enhancing Multilingual Capabilities of Large Language Models through Self-Distillation from Resource-Rich Languages—0
Stealing the Invisible: Unveiling Pre-Trained CNN Models through Adversarial Examples and Timing Side-Channels—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