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 4651–4675 of 10307 papers

TitleStatusHype
Offline Handwriting Signature Verification: A Transfer Learning and Feature Selection Approach—0
Offline-to-online hyperparameter transfer for stochastic bandits—0
oIRL: Robust Adversarial Inverse Reinforcement Learning with Temporally Extended Actions—0
OMNIA Faster R-CNN: Detection in the wild through dataset merging and soft distillation—0
OmniDialog: An Omnipotent Pre-training Model for Task-Oriented Dialogue System—0
Omnidirectional Information Gathering for Knowledge Transfer-based Audio-Visual Navigation—0
OmniPD: One-Step Person Detection in Top-View Omnidirectional Indoor Scenes—0
Omni-Training: Bridging Pre-Training and Meta-Training for Few-Shot Learning—0
On Adversarial Robustness of Language Models in Transfer Learning—0
On-chip Few-shot Learning with Surrogate Gradient Descent on a Neuromorphic Processor—0
On Conditional and Compositional Language Model Differentiable Prompting—0
On consequences of finetuning on data with highly discriminative features—0
On-Device Transfer Learning for Personalising Psychological Stress Modelling using a Convolutional Neural Network—0
One4all User Representation for Recommender Systems in E-commerce—0
On-edge Multi-task Transfer Learning: Model and Practice with Data-driven Task Allocation—0
On effects of Knowledge Distillation on Transfer Learning—0
One for Many: Transfer Learning for Building HVAC Control—0
One Model to Rule them All: Towards Zero-Shot Learning for Databases—0
One-Shot Transfer Learning for Nonlinear ODEs—0
One-stage Modality Distillation for Incomplete Multimodal Learning—0
One Step Is Enough for Few-Shot Cross-Lingual Transfer: Co-Training with Gradient Optimization—0
One System to Rule them All: a Universal Intent Recognition System for Customer Service Chatbots—0
One-To-Many Multilingual End-to-end Speech Translation—0
One-to-Many Semantic Communication Systems: Design, Implementation, Performance Evaluation—0
On evaluating CNN representations for low resource medical image classification—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