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 8026–8050 of 10307 papers

TitleStatusHype
Web image search engine based on LSH index and CNN Resnet50—0
Webly Supervised Learning for Skin Lesion Classification—0
Web-Scale Training for Face Identification—0
Web Table Classification based on Visual Features—0
WebVision Challenge: Visual Learning and Understanding With Web Data—0
WeChat Neural Machine Translation Systems for WMT20—0
WeChat Neural Machine Translation Systems for WMT21—0
Weighted Empirical Risk Minimization: Sample Selection Bias Correction based on Importance Sampling—0
Weighted Empirical Risk Minimization: Transfer Learning based on Importance Sampling—0
Weighted Multisource Tradaboost—0
Weighted Sampling for Masked Language Modeling—0
Weightless Neural Network with Transfer Learning to Detect Distress in Asphalt—0
Weight Squeezing: Reparameterization for Knowledge Transfer and Model Compression—0
Weight Squeezing: Reparameterization for Compression and Fast Inference—0
Weight Squeezing: Reparameterization for Knowledge Transfer and Model Compression—0
Weight subcloning: direct initialization of transformers using larger pretrained ones—0
WESSA at SemEval-2020 Task 9: Code-Mixed Sentiment Analysis using Transformers—0
What can we learn about CNNs from a large scale controlled object dataset?—0
What does it take to bake a cake? The RecipeRef corpus and anaphora resolution in procedural text—0
What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications—0
What Do We Maximize in Self-Supervised Learning?—0
What is Left After Distillation? How Knowledge Transfer Impacts Fairness and Bias—0
What Makes for Hierarchical Vision Transformer?—0
What makes instance discrimination good for transfer learning?—0
What Makes Pre-Trained Visual Representations Successful for Robust Manipulation?—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