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 5951–6000 of 10307 papers

TitleStatusHype
The Impact of Selectional Preference Agreement on Semantic Relational Similarity—0
The Importance of the Instantaneous Phase for classification using Convolutional Neural Networks—0
The Importance of the Instantaneous Phase in Detecting Faces with Convolutional Neural Networks—0
The Information Complexity of Learning Tasks, their Structure and their Distance—0
The iWildCam 2019 Challenge Dataset—0
The JHU/KyotoU Speech Translation System for IWSLT 2018—0
The Joy of Neural Painting—0
The Less the Merrier? Investigating Language Representation in Multilingual Models—0
The LMU Munich System for the WMT20 Very Low Resource Supervised MT Task—0
The Master Key Filters Hypothesis: Deep Filters Are General—0
The Missing Link: Finding label relations across datasets—0
The Multiverse Loss for Robust Transfer Learning—0
The NiuTrans System for the WMT20 Quality Estimation Shared Task—0
The NLP Cookbook: Modern Recipes for Transformer based Deep Learning Architectures—0
The NTNU Taiwanese ASR System for Formosa Speech Recognition Challenge 2020—0
The OCR Quest for Generalization: Learning to recognize low-resource alphabets with model editing—0
The Option Keyboard: Combining Skills in Reinforcement Learning—0
Theoretical Guarantees of Transfer Learning—0
Theoretically-Grounded Policy Advice from Multiple Teachers in Reinforcement Learning Settings with Applications to Negative Transfer—0
On Deep Domain Adaptation: Some Theoretical Understandings—0
Theory-based Causal Transfer: Integrating Instance-level Induction and Abstract-level Structure Learning—0
The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift—0
The Power of Contrast for Feature Learning: A Theoretical Analysis—0
The Power of Training: How Different Neural Network Setups Influence the Energy Demand—0
The Power of Transfer Learning in Agricultural Applications: AgriNet—0
The PV-ALE Dataset: Enhancing Apple Leaf Disease Classification Through Transfer Learning with Convolutional Neural Networks—0
TheraGen: Therapy for Every Generation—0
The Reality of Multi-Lingual Machine Translation—0
The Recurrent Reinforcement Learning Crypto Agent—0
The Relevance of the Source Language in Transfer Learning for ASR—0
Exploring Thermal Images for Object Detection in Underexposure Regions for Autonomous Driving—0
The role of ego vision in view-invariant action recognition—0
The Role of Exploration for Task Transfer in Reinforcement Learning—0
The RWTH Aachen University Machine Translation Systems for WMT 2019—0
The Sample Complexity of Online Strategic Decision Making with Information Asymmetry and Knowledge Transportability—0
10Sent: A Stable Sentiment Analysis Method Based on the Combination of Off-The-Shelf Approaches—0
Flexible deep transfer learning by separate feature embeddings and manifold alignment—0
FLEXIBLE: Forecasting Cellular Traffic by Leveraging Explicit Inductive Graph-Based Learning—0
Flexible Transfer Learning under Support and Model Shift—0
FlexPose: Pose Distribution Adaptation with Limited Guidance—0
Fluorescent Neuronal Cells v2: Multi-Task, Multi-Format Annotations for Deep Learning in Microscopy—0
Focal Cortical Dysplasia Type II Detection Using Cross Modality Transfer Learning and Grad-CAM in 3D-CNNs for MRI Analysis—0
FOI DSS at SemEval-2018 Task 1: Combining LSTM States, Embeddings, and Lexical Features for Affect Analysis—0
Folding membrane proteins by deep transfer learning—0
FoMo: A Foundation Model for Mobile Traffic Forecasting with Diffusion Model—0
Forecasting large-scale circulation regimes using deformable convolutional neural networks and global spatiotemporal climate data—0
Forensic Dental Age Estimation Using Modified Deep Learning Neural Network—0
Forest Inspection Dataset for Aerial Semantic Segmentation and Depth Estimation—0
Forged Image Detection using SOTA Image Classification Deep Learning Methods for Image Forensics with Error Level Analysis—0
Formulation Graphs for Mapping Structure-Composition of Battery Electrolytes to Device Performance—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