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 5676–5700 of 10307 papers

TitleStatusHype
Framework Construction of an Adversarial Federated Transfer Learning Classifier—0
FreeKD: Free-direction Knowledge Distillation for Graph Neural Networks—0
Free speech or Free Hate Speech? Analyzing the Proliferation of Hate Speech in Parler—0
Screening COVID-19 Based on CT/CXR Images & Building a Publicly Available CT-scan Dataset of COVID-19—0
FreeTransfer-X: Safe and Label-Free Cross-Lingual Transfer from Off-the-Shelf Models—0
A survey of underwater acoustic data classification methods using deep learning for shoreline surveillance—0
Freezing the Pivot for Triangular Machine Translation—0
A Survey of the Impact of Self-Supervised Pretraining for Diagnostic Tasks with Radiological Images—0
A Survey of Surface Defect Detection of Industrial Products Based on A Small Number of Labeled Data—0
FReTAL: Generalizing Deepfake Detection using Knowledge Distillation and Representation Learning—0
Active Learning Approaches to Enhancing Neural Machine Translation—0
From Actions to Events: A Transfer Learning Approach Using Improved Deep Belief Networks—0
A Survey of Reinforcement Learning for Optimization in Automation—0
From augmented microscopy to the topological transformer: a new approach in cell image analysis for Alzheimer's research—0
The NLP Cookbook: Modern Recipes for Transformer based Deep Learning Architectures—0
Script Parsing with Hierarchical Sequence Modelling—0
From Fake to Hyperpartisan News Detection Using Domain Adaptation—0
From fat droplets to floating forests: cross-domain transfer learning using a PatchGAN-based segmentation model—0
From FreEM to D'AlemBERT: a Large Corpus and a Language Model for Early Modern French—0
From High-SNR Radar Signal to ECG: A Transfer Learning Model with Cardio-Focusing Algorithm for Scenarios with Limited Data—0
From Isolated Islands to Pangea: Unifying Semantic Space for Human Action Understanding—0
From Large to Super-Tiny: End-to-End Optimization for Cost-Efficient LLMs—0
From Lazy to Rich: Exact Learning Dynamics in Deep Linear Networks—0
From Limited Labels to Open Domains: An Efficient Learning Paradigm for UAV-view Geo-Localization—0
From Macro to Micro: Boosting micro-expression recognition via pre-training on macro-expression videos—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