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 7926–7950 of 10307 papers

TitleStatusHype
Multi-Stage Transfer Learning with an Application to Selection Process—0
Smoothing Adversarial Domain Attack and P-Memory Reconsolidation for Cross-Domain Person Re-Identification—0
Enhanced Transport Distance for Unsupervised Domain Adaptation—0
Distilling Image Dehazing With Heterogeneous Task ImitationCode0
Fast visual grounding in interaction: bringing few-shot learning with neural networks to an interactive robot—0
Unsupervised Word Translation with Adversarial Autoencoder—0
Attribute-Induced Bias Eliminating for Transductive Zero-Shot Learning—0
A Survey on Transfer Learning in Natural Language Processing—0
Critical Assessment of Transfer Learning for Medical Image Segmentation with Fully Convolutional Neural Networks—0
Weight Squeezing: Reparameterization for Compression and Fast Inference—0
Deep Job Understanding at LinkedIn—0
Improving Mammography Malignancy Segmentation by Designing the Training Process—0
Human Recognition Using Face in Computed Tomography—0
Multimodal Feature Fusion and Knowledge-Driven Learning via Experts Consult for Thyroid Nodule Classification—0
CNN-based Approach for Cervical Cancer Classification in Whole-Slide Histopathology ImagesCode0
Contextual Dialogue Act Classification for Open-Domain Conversational AgentsCode0
Universal Lesion Detection by Learning from Multiple Heterogeneously Labeled Datasets—0
Counterfactual Detection meets Transfer LearningCode0
SSM-Net for Plants Disease Identification in Low Data RegimeCode0
Balanced joint maximum mean discrepancy for deep transfer learningCode0
Local Motion Planner for Autonomous Navigation in Vineyards with a RGB-D Camera-Based Algorithm and Deep Learning Synergy—0
Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation ProblemsCode0
Time-Variant Variational Transfer for Value Functions—0
Visual Interest Prediction with Attentive Multi-Task Transfer Learning—0
Automating the Surveillance of Mosquito Vectors from Trapped Specimens Using Computer Vision Techniques—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