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 401–425 of 10307 papers

TitleStatusHype
COVID 19 Diagnosis Analysis using Transfer Learning—0
Realized Volatility Forecasting for New Issues and Spin-Offs using Multi-Source Transfer Learning—0
Effective and Efficient Cross-City Traffic Knowledge Transfer: A Privacy-Preserving Perspective—0
Deepfake Detection of Face Images based on a Convolutional Neural Network—0
Cyclic Contrastive Knowledge Transfer for Open-Vocabulary Object DetectionCode0
TransiT: Transient Transformer for Non-line-of-sight Videography—0
Joint Training And Decoding for Multilingual End-to-End Simultaneous Speech TranslationCode0
Automated Tomato Maturity Estimation Using an Optimized Residual Model with Pruning and Quantization Techniques—0
SOLA-GCL: Subgraph-Oriented Learnable Augmentation Method for Graph Contrastive Learning—0
PyGDA: A Python Library for Graph Domain AdaptationCode3
CleverDistiller: Simple and Spatially Consistent Cross-modal Distillation—0
Teaching LMMs for Image Quality Scoring and InterpretingCode2
Unified Locomotion Transformer with Simultaneous Sim-to-Real Transfer for Quadrupeds—0
Large Language Model as Meta-Surrogate for Data-Driven Many-Task Optimization: A Proof-of-Principle Study—0
External Knowledge Injection for CLIP-Based Class-Incremental LearningCode2
Disentangled World Models: Learning to Transfer Semantic Knowledge from Distracting Videos for Reinforcement Learning—0
Are ECGs enough? Deep learning classification of cardiac anomalies using only electrocardiogramsCode0
Beam Selection in ISAC using Contextual Bandit with Multi-modal Transformer and Transfer Learning—0
Towards species' classification of the Anastrepha pseudoparallela group—0
MMRL: Multi-Modal Representation Learning for Vision-Language ModelsCode2
From Limited Labels to Open Domains: An Efficient Learning Paradigm for UAV-view Geo-Localization—0
Are We Truly Forgetting? A Critical Re-examination of Machine Unlearning Evaluation Protocols—0
MADS: Multi-Attribute Document Supervision for Zero-Shot Image Classification—0
Linguistic Knowledge Transfer Learning for Speech Enhancement—0
Real-Time Load Estimation for Load-lifting Exoskeletons Using Insole Pressure Sensors and Machine Learning—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