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 1276–1300 of 10307 papers

TitleStatusHype
Leveraging Seen and Unseen Semantic Relationships for Generative Zero-Shot LearningCode1
AquaVision: Automating the detection of waste in water bodies using deep transfer learningCode1
On Robustness and Transferability of Convolutional Neural NetworksCode1
Do Adversarially Robust ImageNet Models Transfer Better?Code1
Unsupervised machine learning via transfer learning and k-means clustering to classify materials image dataCode1
Boosting Weakly Supervised Object Detection with Progressive Knowledge TransferCode1
Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restorationCode1
Temporal Self-Ensembling Teacher for Semi-Supervised Object DetectionCode1
TERA: Self-Supervised Learning of Transformer Encoder Representation for SpeechCode1
Adversarially-Trained Deep Nets Transfer Better: Illustration on Image ClassificationCode1
n-Reference Transfer Learning for Saliency PredictionCode1
Domain Adaptation with Auxiliary Target Domain-Oriented ClassifierCode1
SpinalNet: Deep Neural Network with Gradual InputCode1
Transfer Learning for Motor Imagery Based Brain-Computer Interfaces: A Complete PipelineCode1
Language-agnostic BERT Sentence EmbeddingCode1
Rethinking Channel Dimensions for Efficient Model DesignCode1
Transferability of Natural Language Inference to Biomedical Question AnsweringCode1
EndoSLAM Dataset and An Unsupervised Monocular Visual Odometry and Depth Estimation Approach for Endoscopic Videos: Endo-SfMLearnerCode1
Primary Tumor Origin Classification of Lung Nodules in Spectral CT using Transfer LearningCode1
Leveraging Subword Embeddings for Multinational Address ParsingCode1
Asymmetric metric learning for knowledge transferCode1
Generalisable 3D Fabric Architecture for Streamlined Universal Multi-Dataset Medical Image SegmentationCode1
Train and You'll Miss It: Interactive Model Iteration with Weak Supervision and Pre-Trained EmbeddingsCode1
Uncovering the Connections Between Adversarial Transferability and Knowledge TransferabilityCode1
AReLU: Attention-based Rectified Linear UnitCode1
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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