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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 9401–9425 of 10307 papers

TitleStatusHype
Transfer Learning and Organic Computing for Autonomous Vehicles—0
Building medical image classifiers with very limited data using segmentation networks—0
Ensemble of Convolutional Neural Networks for Dermoscopic Images Classification—0
Vendor-independent soft tissue lesion detection using weakly supervised and unsupervised adversarial domain adaptation—0
Automatic Plaque Detection in IVOCT Pullbacks Using Convolutional Neural Networks—0
Intracerebral EEG Artifact Identification Using Convolutional Neural NetworksCode0
A Transfer Learning based Feature-Weak-Relevant Method for Image Clustering—0
Addressee and Response Selection for Multilingual ConversationCode0
Unsupervised Learning of Sentence Representations Using Sequence Consistency—0
Transfer Learning Enhanced Common Spatial Pattern Filtering for Brain Computer Interfaces (BCIs): Overview and a New Approach—0
An Occam's Razor View on Learning Audiovisual Emotion Recognition with Small Training Sets—0
DeePathology: Deep Multi-Task Learning for Inferring Molecular Pathology from Cancer TranscriptomeCode0
Detection and Segmentation of Manufacturing Defects with Convolutional Neural Networks and Transfer Learning—0
Multi-Label Zero-Shot Learning with Transfer-Aware Label Embedding Projection—0
A Survey on Deep Transfer Learning—0
Deep Transfer Learning for EEG-based Brain Computer Interface—0
Autoencoder Based Sample Selection for Self-Taught Learning—0
Smart City Development with Urban Transfer Learning—0
Language Model Supervision for Handwriting Recognition Model Adaptation—0
Multi-task and Multi-lingual Joint Learning of Neural Lexical Utterance Classification based on Partially-shared Modeling—0
Learning Multilingual Topics from Incomparable Corpora—0
Knowledge as A Bridge: Improving Cross-domain Answer Selection with External Knowledge—0
Convolutional Neural Network for Universal Sentence Embeddings—0
Adaptive Multi-Task Transfer Learning for Chinese Word Segmentation in Medical TextCode0
Learning to Progressively Recognize New Named Entities with Sequence to Sequence Models—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