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 6301–6350 of 10307 papers

TitleStatusHype
Model-guided Multi-path Knowledge Aggregation for Aerial Saliency Prediction—0
How Effective is Pre-training of Large Masked Autoencoders for Downstream Earth Observation Tasks?—0
How Far Can We Go with Data Selection? A Case Study on Semantic Sequence Tagging Tasks—0
How Lightweight Can A Vision Transformer Be—0
How many Observations are Enough? Knowledge Distillation for Trajectory Forecasting—0
How much data do I need? A case study on medical data—0
How Much Off-The-Shelf Knowledge Is Transferable From Natural Images To Pathology Images?—0
How Self-Supervised Learning Can be Used for Fine-Grained Head Pose Estimation?—0
How to Adapt Your Large-Scale Vision-and-Language Model—0
How to Make Cross Encoder a Good Teacher for Efficient Image-Text Retrieval?—0
How to Parse a Creole: When Martinican Creole Meets French—0
How to Train Your CheXDragon: Training Chest X-Ray Models for Transfer to Novel Tasks and Healthcare Systems—0
How to Train Your Fact Verifier: Knowledge Transfer with Multimodal Open Models—0
How to Transfer? Zero-Shot Object Recognition via Hierarchical Transfer of Semantic Attributes—0
How transferable are features in convolutional neural network acoustic models across languages?—0
How Transferable are Neural Networks in NLP Applications?—0
How Transferable Are Self-supervised Features in Medical Image Classification Tasks?—0
How Transferable are the Representations Learned by Deep Q Agents?—0
How transfer learning impacts linguistic knowledge in deep NLP models?—0
How we Learn Concepts: A Review of Relevant Advances Since 2010 and Its Inspirations for Teaching—0
How Will It Drape Like? Capturing Fabric Mechanics from Depth Images—0
HPC Storage Service Autotuning Using Variational-Autoencoder-Guided Asynchronous Bayesian Optimization—0
Huawei's NMT Systems for the WMT 2019 Biomedical Translation Task—0
Huawei’s Submissions to the WMT20 Biomedical Translation Task—0
Hub-Pathway: Transfer Learning from A Hub of Pre-trained Models—0
Human-AI Teaming Using Large Language Models: Boosting Brain-Computer Interfacing (BCI) and Brain Research—0
Human-centric Transfer Learning Explanation via Knowledge Graph [Extended Abstract]—0
Human experts vs. machines in taxa recognition—0
Human Gender Prediction Based on Deep Transfer Learning from Panoramic Radiograph Images—0
Human Instruction-Following with Deep Reinforcement Learning via Transfer-Learning from Text—0
Human-in-the-loop online multi-agent approach to increase trustworthiness in ML models through trust scores and data augmentation—0
Human Recognition Using Face in Computed Tomography—0
Human-vehicle Cooperative Visual Perception for Autonomous Driving under Complex Road and Traffic Scenarios—0
Humble your Overconfident Networks: Unlearning Overfitting via Sequential Monte Carlo Tempered Deep Ensembles—0
Humpty Dumpty: Controlling Word Meanings via Corpus Poisoning—0
Hunter NMT System for WMT18 Biomedical Translation Task: Transfer Learning in Neural Machine Translation—0
HUST bearing: a practical dataset for ball bearing fault diagnosis—0
HWNet v2: An Efficient Word Image Representation for Handwritten Documents—0
HW-TSC’s Participation at WMT 2020 Quality Estimation Shared Task—0
Hybrid Classical-Quantum Deep Learning Models for Autonomous Vehicle Traffic Image Classification Under Adversarial Attack—0
Hybrid Classical-Quantum method for Diabetic Foot Ulcer Classification—0
HybridCVLNet: A Hybrid CSI Feedback System and its Domain Adaptation—0
Hybrid deep convolution model for lung cancer detection with transfer learning—0
Hybrid deep learning architecture for general disruption prediction across tokamaks—0
Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images—0
Hybrid Inception Architecture with Residual Connection: Fine-tuned Inception-ResNet Deep Learning Model for Lung Inflammation Diagnosis from Chest Radiographs—0
Hybridized Convolutional Neural Networks and Long Short-Term Memory for Improved Alzheimer's Disease Diagnosis from MRI Scans—0
Hybrid-Learning Video Moment Retrieval across Multi-Domain Labels—0
Hybrid quantum transfer learning for crack image classification on NISQ hardware—0
Hybrid Template Canonical Correlation Analysis Method for Enhancing SSVEP Recognition under data-limited Condition—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