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 10026–10050 of 10307 papers

TitleStatusHype
Google Vizier: A Service for Black-Box OptimizationCode0
Aspect-augmented Adversarial Networks for Domain AdaptationCode0
Deep Neural Networks to Enable Real-time Multimessenger Astrophysics—0
Learning Features by Watching Objects MoveCode0
Transfer Learning based Dynamic Multiobjective Optimization Algorithms—0
A Survey of Inductive Biases for Factorial Representation-Learning—0
Disentangling Space and Time in Video with Hierarchical Variational Auto-encoders—0
AGA: Attribute Guided AugmentationCode0
Multi-source Transfer Learning with Convolutional Neural Networks for Lung Pattern Analysis—0
Core Sampling Framework for Pixel Classification—0
Deep Image Category Discovery using a Transferred Similarity Function—0
Hypothesis Transfer Learning via Transformation Functions—0
Breast Mass Classification from Mammograms using Deep Convolutional Neural NetworksCode0
Transfer Learning via Latent Factor Modeling to Improve Prediction of Surgical Complications—0
Transfer Learning Across Patient Variations with Hidden Parameter Markov Decision Processes—0
Multi-step learning and underlying structure in statistical models—0
Predicting proficiency levels in learner writings by transferring a linguistic complexity model from expert-written coursebooks—0
A Method of Augmenting Bilingual Terminology by Taking Advantage of the Conceptual Systematicity of Terminologies—0
Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel PredictionCode0
Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervision—0
Semi-supervised Learning using Denoising Autoencoders for Brain Lesion Detection and Segmentation—0
Self-learning Scene-specific Pedestrian Detectors using a Progressive Latent Model—0
Nazr-CNN: Fine-Grained Classification of UAV Imagery for Damage Assessment—0
Pruning Convolutional Neural Networks for Resource Efficient InferenceCode0
Cross Domain Knowledge Transfer for Person Re-identification—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