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 2651–2675 of 10307 papers

TitleStatusHype
A 3M-Hybrid Model for the Restoration of Unique Giant Murals: A Case Study on the Murals of Yongle Palace—0
An Evaluation of Transfer Learning for Classifying Sales Engagement Emails at Large Scale—0
Converse Attention Knowledge Transfer for Low-Resource Named Entity Recognition—0
Baby Physical Safety Monitoring in Smart Home Using Action Recognition System—0
An Analysis of Encoder Representations in Transformer-Based Machine Translation—0
BabyNet: Reconstructing 3D faces of babies from uncalibrated photographs—0
A Zero-Shot Learning Approach for Ephemeral Gully Detection from Remote Sensing using Vision Language Models—0
An analysis of data variation and bias in image-based dermatological datasets for machine learning classification—0
Ada-VSR: Adaptive Video Super-Resolution with Meta-Learning—0
A Zero-Shot Generalization Framework for LLM-Driven Cross-Domain Sequential Recommendation—0
A Named Entity Recognition Shootout for German—0
A white-box analysis on the writer-independent dichotomy transformation applied to offline handwritten signature verification—0
Analyzing Urdu Social Media for Sentiments using Transfer Learning with Controlled Translations—0
AdaVocoder: Adaptive Vocoder for Custom Voice—0
A Complete Recipe for Bayesian Knowledge Transfer: Object Tracking—0
Dialogue Strategy Adaptation to New Action Sets Using Multi-dimensional Modelling—0
AdaTrans: Feature-wise and Sample-wise Adaptive Transfer Learning for High-dimensional Regression—0
Analyzing the Variations in Emergency Department Boarding and Testing the Transferability of Forecasting Models across COVID-19 Pandemic Waves in Hong Kong: Hybrid CNN-LSTM approach to quantifying building-level socioecological risk—0
Diagnosis of Skin Cancer Using VGG16 and VGG19 Based Transfer Learning Models—0
Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data—0
Analyzing the Forgetting Problem in Pretrain-Finetuning of Open-domain Dialogue Response Models—0
Diagnosis-oriented Medical Image Compression with Efficient Transfer Learning—0
A visual encoding model based on deep neural networks and transfer learning—0
Analyzing the Evaluation of Cross-Lingual Knowledge Transfer in Multilingual Language Models—0
Analyzing the Effect of Multi-task Learning for Biomedical Named Entity Recognition—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