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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 451–475 of 10307 papers

TitleStatusHype
AVocaDo: Strategy for Adapting Vocabulary to Downstream DomainCode1
Diffusion Models Beat GANs on Image ClassificationCode1
Auxiliary Signal-Guided Knowledge Encoder-Decoder for Medical Report GenerationCode1
Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent AlignmentCode1
2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data SetsCode1
Distance-Based Regularisation of Deep Networks for Fine-TuningCode1
Distilling Knowledge from Graph Convolutional NetworksCode1
Distillation from Heterogeneous Models for Top-K RecommendationCode1
AKHCRNet: Bengali Handwritten Character Recognition Using Deep LearningCode1
Distilling Image Classifiers in Object DetectorsCode1
Distribution-aware Knowledge Prototyping for Non-exemplar Lifelong Person Re-identificationCode1
Do Adversarially Robust ImageNet Models Transfer Better?Code1
AutoTune: Automatically Tuning Convolutional Neural Networks for Improved Transfer LearningCode1
The Surprising Positive Knowledge Transfer in Continual 3D Object Shape ReconstructionCode1
DoG is SGD's Best Friend: A Parameter-Free Dynamic Step Size ScheduleCode1
Domain Adaptation for Time Series Under Feature and Label ShiftsCode1
Avatar Knowledge Distillation: Self-ensemble Teacher Paradigm with UncertaintyCode1
Affordance Transfer Learning for Human-Object Interaction DetectionCode1
A Whisper transformer for audio captioning trained with synthetic captions and transfer learningCode1
Domain Generalization for Prostate Segmentation in Transrectal Ultrasound Images: A Multi-center StudyCode1
Benchmarking Detection Transfer Learning with Vision TransformersCode1
An Empirical Study of Pre-trained Transformers for Arabic Information ExtractionCode1
Drug and Disease Interpretation Learning with Biomedical Entity Representation TransformerCode1
DTBS: Dual-Teacher Bi-directional Self-training for Domain Adaptation in Nighttime Semantic SegmentationCode1
BoolQ: Exploring the Surprising Difficulty of Natural Yes/No QuestionsCode1
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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