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 6126–6150 of 10307 papers

TitleStatusHype
Generative Distribution Prediction: A Unified Approach to Multimodal Learning—0
Generative Image Translation for Data Augmentation of Bone Lesion Pathology—0
Generative Imagination Elevates Machine Translation—0
Generative Knowledge Transfer for Neural Language Models—0
Generative Model-driven Structure Aligning Discriminative Embeddings for Transductive Zero-shot Learning—0
Generative One-Shot Face Recognition—0
Generic Probabilistic Interactive Situation Recognition and Prediction: From Virtual to Real—0
Generic Semi-Supervised Adversarial Subject Translation for Sensor-Based Human Activity Recognition—0
GenFighter: A Generative and Evolutive Textual Attack Removal—0
GENMO: A GENeralist Model for Human MOtion—0
Genomic Language Models: Opportunities and Challenges—0
GeoCLR: Georeference Contrastive Learning for Efficient Seafloor Image Interpretation—0
Geographical Distance Is The New Hyperparameter: A Case Study Of Finding The Optimal Pre-trained Language For English-isiZulu Machine Translation.—0
Geometrically Aligned Transfer Encoder for Inductive Transfer in Regression Tasks—0
Geometrically Regularized Transfer Learning with On-Manifold and Off-Manifold Perturbation—0
Geometric Framework for Cell Oversegmentation—0
Geometric Properties and Graph-Based Optimization of Neural Networks: Addressing Non-Linearity, Dimensionality, and Scalability—0
Geometry-Aware Network for Domain Adaptive Semantic Segmentation—0
Geometry Based Machining Feature Retrieval with Inductive Transfer Learning—0
GeoTransfer : Generalizable Few-Shot Multi-View Reconstruction via Transfer Learning—0
Gesture Recognition in Robotic Surgery: a Review—0
Getting More from Less: Transfer Learning Improves Sleep Stage Decoding Accuracy in Peripheral Wearable Devices—0
GIST: Cross-Domain Click-Through Rate Prediction via Guided Content-Behavior Distillation—0
GistNet: a Geometric Structure Transfer Network for Long-Tailed Recognition—0
Give and Take: Federated Transfer Learning for Industrial IoT Network Intrusion Detection—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