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 1126–1150 of 10307 papers

TitleStatusHype
QDGset: A Large Scale Grasping Dataset Generated with Quality-Diversity—0
In-Context Transfer Learning: Demonstration Synthesis by Transferring Similar TasksCode0
RS-FME-SwinT: A Novel Feature Map Enhancement Framework Integrating Customized SwinT with Residual and Spatial CNN for Monkeypox Diagnosis—0
An Intrinsically Knowledge-Transferring Developmental Spiking Neural Network for Tactile Classification—0
EMGTTL: Transformers-Based Transfer Learning for Classification of ADL using Raw Surface EMG Signals—0
Advanced Arabic Alphabet Sign Language Recognition Using Transfer Learning and Transformer Models—0
Scalable Multi-Task Transfer Learning for Molecular Property Prediction—0
Multi-Scale Convolutional LSTM with Transfer Learning for Anomaly Detection in Cellular Networks—0
On the topology and geometry of population-based SHM—0
FireLite: Leveraging Transfer Learning for Efficient Fire Detection in Resource-Constrained Environments—0
UIR-LoRA: Achieving Universal Image Restoration through Multiple Low-Rank AdaptationCode0
Classroom-Inspired Multi-Mentor Distillation with Adaptive Learning Strategies—0
SurgPETL: Parameter-Efficient Image-to-Surgical-Video Transfer Learning for Surgical Phase Recognition—0
Domain Consistency Representation Learning for Lifelong Person Re-IdentificationCode1
Model Selection with a Shapelet-based Distance Measure for Multi-source Transfer Learning in Time Series ClassificationCode0
OmniXAS: A Universal Deep-Learning Framework for Materials X-ray Absorption SpectraCode0
MedViLaM: A multimodal large language model with advanced generalizability and explainability for medical data understanding and generationCode0
Brain Tumor Classification on MRI in Light of Molecular Markers—0
On the universality of neural encodings in CNNs—0
Accelerating Malware Classification: A Vision Transformer SolutionCode0
Deep Hybrid Architecture for Very Low-Resolution Image Classification Using Capsule AttentionCode0
Meta-RTL: Reinforcement-Based Meta-Transfer Learning for Low-Resource Commonsense Reasoning—0
Towards Diverse Device Heterogeneous Federated Learning via Task Arithmetic Knowledge IntegrationCode0
Harmonizing knowledge Transfer in Neural Network with Unified Distillation—0
How Effective is Pre-training of Large Masked Autoencoders for Downstream Earth Observation Tasks?—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