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 1426–1450 of 10307 papers

TitleStatusHype
Enhancing Wildfire Forecasting Through Multisource Spatio-Temporal Data, Deep Learning, Ensemble Models and Transfer Learning—0
Enhancing Skin Disease Classification Leveraging Transformer-based Deep Learning Architectures and Explainable AI—0
Riemannian Geometry-Based EEG Approaches: A Literature Review—0
A Comparative Study of Transfer Learning for Emotion Recognition using CNN and Modified VGG16 Models—0
Vision-Based Power Line Cables and Pylons Detection for Low Flying Aircraft—0
An Attention-based Representation Distillation Baseline for Multi-Label Continual LearningCode0
Quantifying the value of positive transfer: An experimental case study—0
Dyn-Adapter: Towards Disentangled Representation for Efficient Visual Recognition—0
Straightforward Layer-wise Pruning for More Efficient Visual AdaptationCode0
Rethinking Visual Content Refinement in Low-Shot CLIP AdaptationCode1
Enhancing Graph Neural Networks with Limited Labeled Data by Actively Distilling Knowledge from Large Language Models—0
Semi-Supervised Contrastive Learning of Musical RepresentationsCode1
Are We Ready for Out-of-Distribution Detection in Digital Pathology?—0
MO-EMT-NAS: Multi-Objective Continuous Transfer of Architectural Knowledge Between Tasks from Different Datasets—0
A Scalable and Generalized Deep Learning Framework for Anomaly Detection in Surveillance Videos—0
On Initializing Transformers with Pre-trained Embeddings—0
Green Resource Allocation in Cloud-Native O-RAN Enabled Small Cell Networks—0
Exploring connections of spectral analysis and transfer learning in medical imaging—0
Encapsulating Knowledge in One PromptCode1
MRIo3DS-Net: A Mutually Reinforcing Images to 3D Surface RNN-like framework for model-adaptation indoor 3D reconstruction—0
Novel Artistic Scene-Centric Datasets for Effective Transfer Learning in Fragrant Spaces—0
Genomic Language Models: Opportunities and Challenges—0
LoRA-PT: Low-Rank Adapting UNETR for Hippocampus Segmentation Using Principal Tensor Singular Values and VectorsCode0
Relational Representation DistillationCode1
MapDistill: Boosting Efficient Camera-based HD Map Construction via Camera-LiDAR Fusion Model Distillation—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