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 8851–8875 of 10307 papers

TitleStatusHype
EPITA-ADAPT at SemEval-2019 Task 3: Detecting emotions in textual conversations using deep learning models combination—0
Feature Transfer Learning for Face Recognition With Under-Represented Data—0
SINAI-DL at SemEval-2019 Task 7: Data Augmentation and Temporal Expressions—0
Improving Event Coreference Resolution by Learning Argument Compatibility from Unlabeled Data—0
WUT at SemEval-2019 Task 9: Domain-Adversarial Neural Networks for Domain Adaptation in Suggestion Mining—0
Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain AdaptationCode0
UHH-LT at SemEval-2019 Task 6: Supervised vs. Unsupervised Transfer Learning for Offensive Language Detection—0
Unsupervised Disentangling of Appearance and Geometry by Deformable Generator NetworkCode0
Transfer Learning in Natural Language Processing—0
Time Series Anomaly Detection Using Convolutional Neural Networks and Transfer Learning—0
Fast Solar Image Classification Using Deep Learning and its Importance for Automation in Solar PhysicsCode0
Augmenting Transfer Learning with Semantic Reasoning—0
3DPalsyNet: A Facial Palsy Grading and Motion Recognition Framework using Fully 3D Convolutional Neural Networks—0
A Compare-Aggregate Model with Latent Clustering for Answer Selection—0
On the Generalization Gap in Reparameterizable Reinforcement Learning—0
Deep Cross Networks with Aesthetic Preference for Cross-domain Recommendation—0
Regularization Advantages of Multilingual Neural Language Models for Low Resource Domains—0
Leveraging Medical Visual Question Answering with Supporting Facts—0
SuperTML: Two-Dimensional Word Embedding and Transfer Learning Using ImageNet Pretrained CNN Models for the Classifications on Tabular Data—0
Adversarial Domain Adaptation Being Aware of Class Relationships—0
Style transfer-based image synthesis as an efficient regularization technique in deep learning—0
Application of DenseNet in Camera Model Identification and Post-processing Detection—0
ET-GAN: Cross-Language Emotion Transfer Based on Cycle-Consistent Generative Adversarial Networks—0
MCNE: An End-to-End Framework for Learning Multiple Conditional Network Representations of Social Network—0
XLDA: Cross-Lingual Data Augmentation for Natural Language Inference and Question Answering—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