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 2851–2875 of 10307 papers

TitleStatusHype
Global Neural Networks and The Data Scaling Effect in Financial Time Series ForecastingCode0
Graph Self-Contrast Representation Learning—0
Building a Winning Team: Selecting Source Model Ensembles using a Submodular Transferability Estimation Approach—0
A Survey of the Impact of Self-Supervised Pretraining for Diagnostic Tasks with Radiological Images—0
Parameter and Computation Efficient Transfer Learning for Vision-Language Pre-trained ModelsCode0
SKoPe3D: A Synthetic Dataset for Vehicle Keypoint Perception in 3D from Traffic Monitoring Cameras—0
Transfer Learning between Motor Imagery Datasets using Deep Learning -- Validation of Framework and Comparison of DatasetsCode0
Active flow control for three-dimensional cylinders through deep reinforcement learning—0
Deep Learning Approach for Large-Scale, Real-Time Quantification of Green Fluorescent Protein-Labeled Biological Samples in Microreactors—0
User lung cancer classification using efficientnet from ct scan images—0
Knowledge Graph Embeddings for Multi-Lingual Structured Representations of Radiology Reports—0
QS-TTS: Towards Semi-Supervised Text-to-Speech Synthesis via Vector-Quantized Self-Supervised Speech Representation LearningCode1
Towards Optimal Patch Size in Vision Transformers for Tumor SegmentationCode0
Exploring Cross-Cultural Differences in English Hate Speech Annotations: From Dataset Construction to AnalysisCode0
Target PCA: Transfer Learning Large Dimensional Panel Data—0
Multi-Transfer Learning Techniques for Detecting Auditory Brainstem Response—0
On the Steganographic Capacity of Selected Learning Models—0
Document AI: A Comparative Study of Transformer-Based, Graph-Based Models, and Convolutional Neural Networks For Document Layout AnalysisCode1
A General-Purpose Self-Supervised Model for Computational PathologyCode1
Uncovering the Hidden Cost of Model CompressionCode0
Exploring Model Transferability through the Lens of Potential EnergyCode0
Do the Frankenstein, or how to achieve better out-of-distribution performance with manifold mixing model soup—0
Robust Activity Recognition for Adaptive Worker-Robot Interaction using Transfer Learning—0
LAC: Latent Action Composition for Skeleton-based Action Segmentation—0
PanoSwin: a Pano-style Swin Transformer for Panorama Understanding—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