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 7826–7850 of 10307 papers

TitleStatusHype
Multilabel Classification for Lung Disease Detection: Integrating Deep Learning and Natural Language Processing—0
Spatio-Temporal Crop Aggregation for Video Representation Learning—0
Multi-Label Classification on Remote-Sensing Images—0
A Centralized-Distributed Transfer Model for Cross-Domain Recommendation Based on Multi-Source Heterogeneous Transfer Learning—0
Multi-label Learning Based Deep Transfer Neural Network for Facial Attribute Classification—0
All Birds with One Stone: Multi-task Learning for Inference with One Forward Pass—0
Multi-Label Transfer Learning for Multi-Relational Semantic Similarity—0
Multi-label Zero-shot Classification by Learning to Transfer from External Knowledge—0
Multi-Label Zero-Shot Human Action Recognition via Joint Latent Ranking Embedding—0
Multi-Label Zero-Shot Learning with Transfer-Aware Label Embedding Projection—0
Spatiotemporal Modeling for Crowd Counting in Videos—0
Label-aware Multi-level Contrastive Learning for Cross-lingual Spoken Language Understanding—0
Multi-level datasets training method in Physics-Informed Neural Networks—0
Multi-Level Fine-Tuning, Data Augmentation, and Few-Shot Learning for Specialized Cyber Threat Intelligence—0
Spatio-Temporal Multi-Subgraph GCN for 3D Human Motion Prediction—0
Improved Techniques for Quantizing Deep Networks with Adaptive Bit-Widths—0
Multilevel Knowledge Transfer for Cross-Domain Object Detection—0
SatSwinMAE: Efficient Autoencoding for Multiscale Time-series Satellite Imagery—0
ALLaM: Large Language Models for Arabic and English—0
Multilingual Argument Mining: Datasets and Analysis—0
A3E: Aligned and Augmented Adversarial Ensemble for Accurate, Robust and Privacy-Preserving EEG Decoding—0
Accurate Prostate Cancer Detection and Segmentation on Biparametric MRI using Non-local Mask R-CNN with Histopathological Ground Truth—0
Multilingual Dependency Parsing for Low-Resource African Languages: Case Studies on Bambara, Wolof, and Yoruba—0
Multilingual Document-Level Translation Enables Zero-Shot Transfer From Sentences to Documents—0
Multilingual Document-Level Translation Enables Zero-Shot Transfer From Sentences to Documents—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