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 6151–6175 of 10307 papers

TitleStatusHype
Pre-text Representation Transfer for Deep Learning with Limited Imbalanced Data : Application to CT-based COVID-19 Detection—0
Backdoor Attacks against Transfer Learning with Pre-trained Deep Learning Models—0
Backdoor Attacks Against Deep Learning Systems in the Physical World—0
Pretrained language model transfer on neural named entity recognition in Indonesian conversational texts—0
Pre-Trained Model Recommendation for Downstream Fine-tuning—0
Pre-Trained Models: Past, Present and Future—0
Pre-trained Word Embeddings for Goal-conditional Transfer Learning in Reinforcement Learning—0
Pre-training Auto-regressive Robotic Models with 4D Representations—0
Pretraining boosts out-of-domain robustness for pose estimation—0
Pretraining for Conditional Generation with Pseudo Self Attention—0
Pre-Training Graph Contrastive Masked Autoencoders are Strong Distillers for EEG—0
Pre-training Tensor-Train Networks Facilitates Machine Learning with Variational Quantum Circuits—0
Pre-training Text-to-Text Transformers to Write and Reason with Concepts—0
Pre-training transformer-based framework on large-scale pediatric claims data for downstream population-specific tasks—0
Pre-training Transformers for Molecular Property Prediction Using Reaction Prediction—0
Background-Foreground Segmentation for Interior Sensing in Automotive Industry—0
BactInt: A domain driven transfer learning approach and a corpus for extracting inter-bacterial interactions from biomedical text—0
Understanding and Improving Transfer Learning of Deep Models via Neural Collapse—0
BadPre: Task-agnostic Backdoor Attacks to Pre-trained NLP Foundation Models—0
Prioritizing Potential Wetland Areas via Region-to-Region Knowledge Transfer and Adaptive Propagation—0
Prior Knowledge for Few-shot Learning—Inductive Reasoning and Distribution Calibration—0
Priors, Hierarchy, and Information Asymmetry for Skill Transfer in Reinforcement Learning—0
Privacy Analysis of Deep Learning in the Wild: Membership Inference Attacks against Transfer Learning—0
Bailong: Bilingual Transfer Learning based on QLoRA and Zip-tie Embedding—0
Privacy-Preserving Ensemble Infused Enhanced Deep Neural Network Framework for Edge Cloud Convergence—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