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 701–725 of 10307 papers

TitleStatusHype
Uncovering the Connections Between Adversarial Transferability and Knowledge TransferabilityCode1
The Surprising Positive Knowledge Transfer in Continual 3D Object Shape ReconstructionCode1
Benchmarking Detection Transfer Learning with Vision TransformersCode1
A Whisper transformer for audio captioning trained with synthetic captions and transfer learningCode1
DoubleU-Net: A Deep Convolutional Neural Network for Medical Image SegmentationCode1
Do Vision Transformers See Like Convolutional Neural Networks?Code1
BadMerging: Backdoor Attacks Against Model MergingCode1
DTL: Disentangled Transfer Learning for Visual RecognitionCode1
Dual-Teacher++: Exploiting Intra-domain and Inter-domain Knowledge with Reliable Transfer for Cardiac SegmentationCode1
Dual Transfer Learning for Event-based End-task Prediction via Pluggable Event to Image TranslationCode1
A Comprehensive Study on Torchvision Pre-trained Models for Fine-grained Inter-species ClassificationCode1
Dynamic Domain Adaptation for Efficient InferenceCode1
A Visual Analytics Framework for Explaining and Diagnosing Transfer Learning ProcessesCode1
Global Self-Attention as a Replacement for Graph ConvolutionCode1
EENLP: Cross-lingual Eastern European NLP IndexCode1
EEV: A Large-Scale Dataset for Studying Evoked Expressions from VideoCode1
Efficient Adaptation of Large Vision Transformer via Adapter Re-ComposingCode1
Efficient Conditional GAN Transfer with Knowledge Propagation across ClassesCode1
An Encoder-Decoder Based Audio Captioning System With Transfer and Reinforcement LearningCode1
An Empirical Analysis of Image-Based Learning Techniques for Malware ClassificationCode1
Efficient parametrization of multi-domain deep neural networksCode1
AVocaDo: Strategy for Adapting Vocabulary to Downstream DomainCode1
Bag of Tricks for Image Classification with Convolutional Neural NetworksCode1
An Empirical Investigation of Model-to-Model Distribution Shifts in Trained Convolutional FiltersCode1
Bert4XMR: Cross-Market Recommendation with Bidirectional Encoder Representations from TransformerCode1
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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