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 4051–4075 of 10307 papers

TitleStatusHype
Motion-Guided Masking for Spatiotemporal Representation Learning—0
Multitasking Evolutionary Algorithm Based on Adaptive Seed Transfer for Combinatorial Problem—0
Source-Free Collaborative Domain Adaptation via Multi-Perspective Feature Enrichment for Functional MRI AnalysisCode0
Exploring the Optimization Objective of One-Class Classification for Anomaly Detection—0
Efficient Transfer Learning in Diffusion Models via Adversarial Noise—0
Knowledge-Aware Prompt Tuning for Generalizable Vision-Language Models—0
Addressing Dynamic and Sparse Qualitative Data: A Hilbert Space Embedding of Categorical Variables—0
Masked Momentum Contrastive Learning for Zero-shot Semantic Understanding—0
Ear-Keeper: Real-time Diagnosis of Ear Lesions Utilizing Ultralight-Ultrafast ConvNet and Large-scale Ear Endoscopic Dataset—0
Rethinking Transfer and Auxiliary Learning for Improving Audio Captioning Transformer—0
Omnidirectional Information Gathering for Knowledge Transfer-based Audio-Visual Navigation—0
VLN-PETL: Parameter-Efficient Transfer Learning for Vision-and-Language NavigationCode0
Large Transformers are Better EEG LearnersCode0
Knowledge Transfer from High-Resource to Low-Resource Programming Languages for Code LLMs—0
Eva-KELLM: A New Benchmark for Evaluating Knowledge Editing of LLMs—0
Prototypical Cross-domain Knowledge Transfer for Cervical Dysplasia Visual Inspection—0
Dual Branch Deep Learning Network for Detection and Stage Grading of Diabetic Retinopathy—0
Disposable Transfer Learning for Selective Source Task Unlearning—0
Towards a High-Performance Object Detector: Insights from Drone Detection Using ViT and CNN-based Deep Learning Models—0
A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery—0
Time Series Predictions in Unmonitored Sites: A Survey of Machine Learning Techniques in Water Resources—0
Improving Buoy Detection with Deep Transfer Learning for Mussel Farm Automation—0
Bridged-GNN: Knowledge Bridge Learning for Effective Knowledge Transfer—0
SimDA: Simple Diffusion Adapter for Efficient Video Generation—0
Deformable-Detection Transformer for Microbubble Localization in Ultrasound Localization Microscopy—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