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 5951–5975 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
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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