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 676–700 of 10307 papers

TitleStatusHype
DeepShadows: Separating Low Surface Brightness Galaxies from Artifacts using Deep LearningCode1
Deep Subdomain Adaptation Network for Image ClassificationCode1
Deep Transfer Learning Baselines for Sentiment Analysis in RussianCode1
Bag of Tricks for Image Classification with Convolutional Neural NetworksCode1
DeiT III: Revenge of the ViTCode1
Delving into Masked Autoencoders for Multi-Label Thorax Disease ClassificationCode1
Denoised Self-Augmented Learning for Social RecommendationCode1
Algorithmic encoding of protected characteristics in image-based models for disease detectionCode1
DenseShift: Towards Accurate and Efficient Low-Bit Power-of-Two QuantizationCode1
DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuningCode1
A Whisper transformer for audio captioning trained with synthetic captions and transfer learningCode1
BARThez: a Skilled Pretrained French Sequence-to-Sequence ModelCode1
Developing a Named Entity Recognition Dataset for TagalogCode1
Development and bilingual evaluation of Japanese medical large language model within reasonably low computational resourcesCode1
Anatomical Foundation Models for Brain MRIsCode1
Avatar Knowledge Distillation: Self-ensemble Teacher Paradigm with UncertaintyCode1
Auxiliary Signal-Guided Knowledge Encoder-Decoder for Medical Report GenerationCode1
A Comprehensive Approach for UAV Small Object Detection with Simulation-based Transfer Learning and Adaptive FusionCode1
Diffusion Model as Representation LearnerCode1
Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent AlignmentCode1
Disentangled Knowledge Transfer for OOD Intent Discovery with Unified Contrastive LearningCode1
Disentangling Spatial and Temporal Learning for Efficient Image-to-Video Transfer LearningCode1
Distance-Based Regularisation of Deep Networks for Fine-TuningCode1
Distillation from Heterogeneous Models for Top-K RecommendationCode1
A Visual Analytics Framework for Explaining and Diagnosing Transfer Learning ProcessesCode1
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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