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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4876–4900 of 10307 papers

TitleStatusHype
Toward Improved Generalization: Meta Transfer of Self-supervised Knowledge on Graphs—0
Silhouette: Toward Performance-Conscious and Transferable CPU Embeddings—0
Colab NAS: Obtaining lightweight task-specific convolutional neural networks following Occam's razorCode0
A New Deep Boosted CNN and Ensemble Learning based IoT Malware Detection—0
Improving generalization in reinforcement learning through forked agents—0
HeartBEiT: Vision Transformer for Electrocardiogram Data Improves Diagnostic Performance at Low Sample Sizes—0
An adaptive human-in-the-loop approach to emission detection of Additive Manufacturing processes and active learning with computer vision—0
Evolutionary Multitasking with Solution Space Cutting for Point Cloud Registration—0
Synthetic Image Data for Deep Learning—0
Transfer Learning using Spectral Convolutional Autoencoders on Semi-Regular Surface MeshesCode0
End-to-End Speech Translation of Arabic to English Broadcast News—0
Augmenting Knowledge Transfer across GraphsCode0
An AI-Powered VVPAT Counter for Elections in IndiaCode0
EEG-NeXt: A Modernized ConvNet for The Classification of Cognitive Activity from EEG—0
Low-Resource End-to-end Sanskrit TTS using Tacotron2, WaveGlow and Transfer Learning—0
Pivotal Role of Language Modeling in Recommender Systems: Enriching Task-specific and Task-agnostic Representation Learning—0
EASpace: Enhanced Action Space for Policy TransferCode0
An Efficient Evolutionary Deep Learning Framework Based on Multi-source Transfer Learning to Evolve Deep Convolutional Neural Networks—0
TweetDrought: A Deep-Learning Drought Impacts Recognizer based on Twitter Data—0
Zero-Shot Transfer Learning for Structural Health Monitoring using Generative Adversarial Networks and Spectral MappingCode0
Transfer learning for chemically accurate interatomic neural network potentialsCode0
CURO: Curriculum Learning for Relative Overgeneralization—0
Cross-Domain Few-Shot Relation Extraction via Representation Learning and Domain Adaptation—0
MobileTL: On-device Transfer Learning with Inverted Residual Blocks—0
Minimum Class Confusion based Transfer for Land Cover Segmentation in Rural and Urban Regions—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