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 7301–7350 of 10307 papers

TitleStatusHype
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
The Sandwich meta-framework for architecture agnostic deep privacy-preserving transfer learning for non-invasive brainwave decoding—0
Transfer Learning for Mixed-Integer Resource Allocation Problems in Wireless Networks—0
Understanding the efficacy, reliability and resiliency of computer vision techniques for malware detection and future research directions—0
An Evaluation of Transfer Learning for Classifying Sales Engagement Emails at Large Scale—0
Tuned Inception V3 for Recognizing States of Cooking Ingredients—0
SuperChat: Dialogue Generation by Transfer Learning from Vision to Language using Two-dimensional Word Embedding and Pretrained ImageNet CNN Models—0
Anomaly Detection in Images—0
Deep Face Recognition Model Compression via Knowledge Transfer and Distillation—0
Deep Feature Learning from a Hospital-Scale Chest X-ray Dataset with Application to TB Detection on a Small-Scale Dataset—0
Regularization Advantages of Multilingual Neural Language Models for Low Resource Domains—0
P2L: Predicting Transfer Learning for Images and Semantic Relations—0
MaLTESE: Large-Scale Simulation-Driven Machine Learning for Transient Driving Cycles—0
How to improve CNN-based 6-DoF camera pose estimation—0
Model-Based and Data-Driven Strategies in Medical Image Computing—0
Hydrocephalus verification on brain magnetic resonance images with deep convolutional neural networks and "transfer learning" technique—0
Transfer Learning with Edge Attention for Prostate MRI Segmentation—0
Evolutionary Optimization of 1D-CNN for Non-contact Respiration Pattern Classification—0
1-D Convolutional Graph Convolutional Networks for Fault Detection in Distributed Energy Systems—0
Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomy—0
Domain Shift Analysis in Chest Radiographs Classification in a Veterans Healthcare Administration Population—0
An Explainable Vision Transformer with Transfer Learning Combined with Support Vector Machine Based Efficient Drought Stress Identification—0
Efficient Patient Fine-Tuned Seizure Detection with a Tensor Kernel Machine—0
A deep learning-enabled smart garment for accurate and versatile sleep conditions monitoring in daily life—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