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 2751–2775 of 10307 papers

TitleStatusHype
OceanBench: The Sea Surface Height EditionCode1
Confidence-based Visual Dispersal for Few-shot Unsupervised Domain AdaptationCode1
Question answering using deep learning in low resource Indian language Marathi—0
VideoAdviser: Video Knowledge Distillation for Multimodal Transfer Learning—0
Cross-Modal Multi-Tasking for Speech-to-Text Translation via Hard Parameter Sharing—0
Cross-Dataset Experimental Study of Radar-Camera Fusion in Bird's-Eye View—0
Robust Internal Representations for Domain Generalization—0
Boosting High Resolution Image Classification with Scaling-up TransformersCode0
Facilitating Interdisciplinary Knowledge Transfer with Research Paper Recommender SystemsCode0
ADU-Depth: Attention-based Distillation with Uncertainty Modeling for Depth Estimation—0
XGV-BERT: Leveraging Contextualized Language Model and Graph Neural Network for Efficient Software Vulnerability Detection—0
BLIP-Adapter: Parameter-Efficient Transfer Learning for Mobile Screenshot CaptioningCode0
Event Stream-based Visual Object Tracking: A High-Resolution Benchmark Dataset and A Novel BaselineCode2
An Ensemble Model for Distorted Images in Real Scenarios—0
DistillBEV: Boosting Multi-Camera 3D Object Detection with Cross-Modal Knowledge DistillationCode1
Transferring climate change physical knowledgeCode0
ALEX: Towards Effective Graph Transfer Learning with Noisy Labels—0
Physics-Informed Solution of The Stationary Fokker-Plank Equation for a Class of Nonlinear Dynamical Systems: An Evaluation Study—0
Incorporating Ensemble and Transfer Learning For An End-To-End Auto-Colorized Image Detection Model—0
Unveiling the Potential of Deep Learning Models for Solar Flare Prediction in Near-Limb Regions—0
SINCERE: Supervised Information Noise-Contrastive Estimation REvisitedCode0
GraphAdapter: Tuning Vision-Language Models With Dual Knowledge GraphCode1
Comparative Evaluation of Transfer Learning for Classification of Brain Tumor Using MRI—0
Cross-modal Alignment with Optimal Transport for CTC-based ASR—0
A Text Classification-Based Approach for Evaluating and Enhancing the Machine Interpretability of Building CodesCode1
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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