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 2826–2850 of 10307 papers

TitleStatusHype
Straightforward Layer-wise Pruning for More Efficient Visual AdaptationCode0
An Attention-based Representation Distillation Baseline for Multi-Label Continual LearningCode0
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
Enhancing Graph Neural Networks with Limited Labeled Data by Actively Distilling Knowledge from Large Language Models—0
Vision-Based Power Line Cables and Pylons Detection for Low Flying Aircraft—0
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
LoRA-PT: Low-Rank Adapting UNETR for Hippocampus Segmentation Using Principal Tensor Singular Values and VectorsCode0
MapDistill: Boosting Efficient Camera-based HD Map Construction via Camera-LiDAR Fusion Model Distillation—0
MRIo3DS-Net: A Mutually Reinforcing Images to 3D Surface RNN-like framework for model-adaptation indoor 3D reconstruction—0
Exploring connections of spectral analysis and transfer learning in medical imaging—0
Green Resource Allocation in Cloud-Native O-RAN Enabled Small Cell Networks—0
Genomic Language Models: Opportunities and Challenges—0
Cross-Phase Mutual Learning Framework for Pulmonary Embolism Identification on Non-Contrast CT Scans—0
Novel Artistic Scene-Centric Datasets for Effective Transfer Learning in Fragrant Spaces—0
Heterogenous Multi-Source Data Fusion Through Input Mapping and Latent Variable Gaussian Process—0
Exploration in Knowledge Transfer Utilizing Reinforcement Learning—0
Deep-Learning-Based Markerless Pose Estimation Systems in Gait Analysis: DeepLabCut Custom Training and the Refinement Function—0
Order parameters and phase transitions of continual learning in deep neural networks—0
Automated detection of gibbon calls from passive acoustic monitoring data using convolutional neural networks in the "torch for R" ecosystem—0
Combining Federated Learning and Control: A Survey—0
Tissue-Contrastive Semi-Masked Autoencoders for Segmentation Pretraining on Chest CT—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