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 1901–1950 of 10307 papers

TitleStatusHype
Realized Volatility Forecasting for New Issues and Spin-Offs using Multi-Source Transfer Learning—0
Effective and Efficient Cross-City Traffic Knowledge Transfer: A Privacy-Preserving Perspective—0
Joint Training And Decoding for Multilingual End-to-End Simultaneous Speech TranslationCode0
Cyclic Contrastive Knowledge Transfer for Open-Vocabulary Object DetectionCode0
Deepfake Detection of Face Images based on a Convolutional Neural Network—0
TransiT: Transient Transformer for Non-line-of-sight Videography—0
Automated Tomato Maturity Estimation Using an Optimized Residual Model with Pruning and Quantization Techniques—0
SOLA-GCL: Subgraph-Oriented Learnable Augmentation Method for Graph Contrastive Learning—0
CleverDistiller: Simple and Spatially Consistent Cross-modal Distillation—0
Unified Locomotion Transformer with Simultaneous Sim-to-Real Transfer for Quadrupeds—0
Are ECGs enough? Deep learning classification of cardiac anomalies using only electrocardiogramsCode0
Disentangled World Models: Learning to Transfer Semantic Knowledge from Distracting Videos for Reinforcement Learning—0
Towards species' classification of the Anastrepha pseudoparallela group—0
Beam Selection in ISAC using Contextual Bandit with Multi-modal Transformer and Transfer Learning—0
Large Language Model as Meta-Surrogate for Data-Driven Many-Task Optimization: A Proof-of-Principle Study—0
From Limited Labels to Open Domains: An Efficient Learning Paradigm for UAV-view Geo-Localization—0
Are We Truly Forgetting? A Critical Re-examination of Machine Unlearning Evaluation Protocols—0
Real-Time Load Estimation for Load-lifting Exoskeletons Using Insole Pressure Sensors and Machine Learning—0
Linguistic Knowledge Transfer Learning for Speech Enhancement—0
MADS: Multi-Attribute Document Supervision for Zero-Shot Image Classification—0
HFedCKD: Toward Robust Heterogeneous Federated Learning via Data-free Knowledge Distillation and Two-way Contrast—0
MoFE: Mixture of Frozen Experts Architecture—0
MetaXCR: Reinforcement-Based Meta-Transfer Learning for Cross-Lingual Commonsense Reasoning—0
Asymmetric Decision-Making in Online Knowledge Distillation:Unifying Consensus and Divergence—0
Transfer Learning for LQR Control—0
Text-Speech Language Models with Improved Cross-Modal Transfer by Aligning Abstraction Levels—0
Data-Free Black-Box Federated Learning via Zeroth-Order Gradient Estimation—0
Adversarial Robustness of Discriminative Self-Supervised Learning in Vision—0
NeuroADDA: Active Discriminative Domain Adaptation in Connectomic—0
CACTUS: An Open Dataset and Framework for Automated Cardiac Assessment and Classification of Ultrasound Images Using Deep Transfer Learning—0
Statistical Deficiency for Task Inclusion Estimation—0
Partially Supervised Unpaired Multi-Modal Learning for Label-Efficient Medical Image Segmentation—0
opXRD: Open Experimental Powder X-ray Diffraction Database—0
Implicit Cross-Lingual Rewarding for Efficient Multilingual Preference AlignmentCode0
TinyR1-32B-Preview: Boosting Accuracy with Branch-Merge Distillation—0
Sarcasm Detection as a Catalyst: Improving Stance Detection with Cross-Target Capabilities—0
Intermediate-Task Transfer Learning: Leveraging Sarcasm Detection for Stance Detection—0
PGAD: Prototype-Guided Adaptive Distillation for Multi-Modal Learning in AD Diagnosis—0
A Quantum Neural Network Transfer-Learning Model for Forecasting Problems with Continuous and Discrete Variables—0
X2CT-CLIP: Enable Multi-Abnormality Detection in Computed Tomography from Chest Radiography via Tri-Modal Contrastive Learning—0
Remote Sensing Image Classification Using Convolutional Neural Network (CNN) and Transfer Learning Techniques—0
Teaching AI to Handle Exceptions: Supervised Fine-Tuning with Human-Aligned Judgment—0
AugFL: Augmenting Federated Learning with Pretrained ModelsCode0
Hyperspectral Image Restoration and Super-resolution with Physics-Aware Deep Learning for Biomedical Applications—0
Conditional Electrocardiogram Generation Using Hierarchical Variational Autoencoders—0
Diagnosis of Patients with Viral, Bacterial, and Non-Pneumonia Based on Chest X-Ray Images Using Convolutional Neural Networks—0
An Efficient Approach to Detecting Lung Nodules Using Swin Transformer—0
A Zero-Shot Learning Approach for Ephemeral Gully Detection from Remote Sensing using Vision Language Models—0
A Transfer Framework for Enhancing Temporal Graph Learning in Data-Scarce Settings—0
Towards Understanding the Benefit of Multitask Representation Learning in Decision Process—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