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 4201–4225 of 10307 papers

TitleStatusHype
A Comparative Study of Transfer Learning for Emotion Recognition using CNN and Modified VGG16 Models—0
Data Augmentation using Feature Generation for Volumetric Medical Images—0
Data Augmentation for End-to-End Speech Translation: FBK@IWSLT ‘19—0
Autism Disease Detection Using Transfer Learning Techniques: Performance Comparison Between Central Processing Unit vs Graphics Processing Unit Functions for Neural Networks—0
E-Stitchup: Data Augmentation for Pre-Trained Embeddings—0
Data Augmentation and Transfer Learning Approaches Applied to Facial Expressions Recognition—0
A Modular and Unified Framework for Detecting and Localizing Video Anomalies—0
GraphControl: Adding Conditional Control to Universal Graph Pre-trained Models for Graph Domain Transfer Learning—0
Data Annealing for Informal Language Understanding Tasks—0
Data-adaptive Transfer Learning for Low-resource Translation: A Case Study in Haitian—0
Authorship Attribution in Bangla Literature (AABL) via Transfer Learning using ULMFiT—0
Data-adaptive Transfer Learning for Translation: A Case Study in Haitian and Jamaican—0
A Universal Parent Model for Low-Resource Neural Machine Translation Transfer—0
A Modular and Transferable Reinforcement Learning Framework for the Fleet Rebalancing Problem—0
DASGrad: Double Adaptive Stochastic Gradient—0
Explanation and Use of Uncertainty Quantified by Bayesian Neural Network Classifiers for Breast Histopathology Images—0
DARTS: Dialectal Arabic Transcription System—0
Dark Reciprocal-Rank: Boosting Graph-Convolutional Self-Localization Network via Teacher-to-student Knowledge Transfer—0
A Unified View of Abstract Visual Reasoning Problems—0
Adaptive Prototype Knowledge Transfer for Federated Learning with Mixed Modalities and Heterogeneous Tasks—0
DaRec: A Disentangled Alignment Framework for Large Language Model and Recommender System—0
DARE: A large-scale handwritten date recognition system—0
Review-Based Domain Disentanglement without Duplicate Users or Contexts for Cross-Domain Recommendation—0
A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendation—0
SAPT: A Shared Attention Framework for Parameter-Efficient Continual Learning of Large Language Models—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