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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 8276–8300 of 10307 papers

TitleStatusHype
A Fully Automated System for Sizing Nasal PAP Masks Using Facial Photographs—0
A Cantor-Kantorovich Metric Between Markov Decision Processes with Application to Transfer Learning—0
Stochastic analysis of heterogeneous porous material with modified neural architecture search (NAS) based physics-informed neural networks using transfer learning—0
One-Shot Transfer Learning for Nonlinear ODEs—0
Improving the Behaviour of Vision Transformers with Token-consistent Stochastic Layers—0
A Free-Energy Principle for Representation Learning—0
A Framework of Meta Functional Learning for Regularising Knowledge Transfer—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
On Explaining Knowledge Distillation: Measuring and Visualising the Knowledge Transfer Process—0
On Generality and Knowledge Transferability in Cross-Domain Duplicate Question Detection for Heterogeneous Community Question Answering—0
Stochastic Precision Ensemble: Self-Knowledge Distillation for Quantized Deep Neural Networks—0
On Giant's Shoulders: Effortless Weak to Strong by Dynamic Logits Fusion—0
On Improving the Performance of Glitch Classification for Gravitational Wave Detection by using Generative Adversarial Networks—0
Stochastic Vision Transformers with Wasserstein Distance-Aware Attention—0
On Initializing Transformers with Pre-trained Embeddings—0
On Knowledge Distillation for Direct Speech Translation—0
A Framework of Customer Review Analysis Using the Aspect-Based Opinion Mining Approach—0
On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sources—0
A Framework for Hierarchical Multilingual Machine Translation—0
Online-Adaptive Anomaly Detection for Defect Identification in Aircraft Assembly—0
Show:102550
← PrevPage 332 of 413Next →
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