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 1551–1575 of 10307 papers

TitleStatusHype
LLM4CP: Adapting Large Language Models for Channel PredictionCode3
Learning to Transfer for Evolutionary Multitasking—0
Depth F_1: Improving Evaluation of Cross-Domain Text Classification by Measuring Semantic GeneralizabilityCode0
Multi-modal Transfer Learning between Biological Foundation Models—0
Data-Centric AI in the Age of Large Language Models—0
Generalization error of min-norm interpolators in transfer learning—0
Robust Few-shot Transfer Learning for Knowledge Base Question Answering with Unanswerable Questions—0
Information Guided Regularization for Fine-tuning Language ModelsCode0
Probabilistic Deep Learning and Transfer Learning for Robust Cryptocurrency Price PredictionCode0
Modeling & Evaluating the Performance of Convolutional Neural Networks for Classifying Steel Surface Defects—0
Robust Melanoma Thickness Prediction via Deep Transfer Learning enhanced by XAI Techniques—0
Towards Trustworthy Unsupervised Domain Adaptation: A Representation Learning Perspective for Enhancing Robustness, Discrimination, and Generalization—0
WONDERBREAD: A Benchmark for Evaluating Multimodal Foundation Models on Business Process Management TasksCode1
Neuro-symbolic Training for Reasoning over Spatial LanguageCode0
CNN Based Flank Predictor for Quadruped Animal Species—0
Machine Learning Based Prediction of Proton Conductivity in Metal-Organic Frameworks—0
Causal Discovery Inspired Unsupervised Domain Adaptation for Emotion-Cause Pair Extraction—0
Skin Cancer Images Classification using Transfer Learning Techniques—0
BIOSCAN-5M: A Multimodal Dataset for Insect BiodiversityCode1
The Wisdom of a Crowd of Brains: A Universal Brain Encoder—0
Spatial Sequence Attention Network for Schizophrenia Classification from Structural Brain MR Images—0
Depth Anywhere: Enhancing 360 Monocular Depth Estimation via Perspective Distillation and Unlabeled Data Augmentation—0
Automated MRI Quality Assessment of Brain T1-weighted MRI in Clinical Data Warehouses: A Transfer Learning Approach Relying on Artefact SimulationCode2
Latent Intuitive Physics: Learning to Transfer Hidden Physics from A 3D Video—0
Online-Adaptive Anomaly Detection for Defect Identification in Aircraft Assembly—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