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 1401–1425 of 10307 papers

TitleStatusHype
Learn from the Learnt: Source-Free Active Domain Adaptation via Contrastive Sampling and Visual PersistenceCode0
GLAM: Glomeruli Segmentation for Human Pathological Lesions using Adapted Mouse Model—0
Exploring the Effect of Dataset Diversity in Self-Supervised Learning for Surgical Computer VisionCode2
Advancing 3D Point Cloud Understanding through Deep Transfer Learning: A Comprehensive Survey—0
Difficulty Estimation and Simplification of French Text Using LLMs—0
Innovative Speech-Based Deep Learning Approaches for Parkinson's Disease Classification: A Systematic Review—0
How Lightweight Can A Vision Transformer Be—0
Detection of manatee vocalisations using the Audio Spectrogram TransformerCode0
Peak-Controlled Logits Poisoning Attack in Federated Distillation—0
Label Alignment and Reassignment with Generalist Large Language Model for Enhanced Cross-Domain Named Entity Recognition—0
Wavelet-based Autoencoder and EfficientNet for Schizophrenia Detection from EEG Signals—0
Forecasting Credit Ratings: A Case Study where Traditional Methods Outperform Generative LLMsCode0
Federated Automatic Latent Variable Selection in Multi-output Gaussian Processes—0
ODGR: Online Dynamic Goal Recognition—0
AbdomenAtlas: A Large-Scale, Detailed-Annotated, & Multi-Center Dataset for Efficient Transfer Learning and Open Algorithmic BenchmarkingCode3
Towards scalable efficient on-device ASR with transfer learning—0
EffiSegNet: Gastrointestinal Polyp Segmentation through a Pre-Trained EfficientNet-based Network with a Simplified DecoderCode1
Exploring the Effectiveness and Consistency of Task Selection in Intermediate-Task Transfer LearningCode0
Generalizing Teacher Networks for Effective Knowledge Distillation Across Student ArchitecturesCode0
Affordance Labeling and Exploration: A Manifold-Based Approach—0
TreeSBA: Tree-Transformer for Self-Supervised Sequential Brick AssemblyCode0
ALLaM: Large Language Models for Arabic and English—0
Reconstructing Training Data From Real World Models Trained with Transfer Learning—0
Few-Shot Transfer Learning for Individualized Braking Intent Detection on Neuromorphic Hardware—0
Practical multi-fidelity machine learning: fusion of deterministic and Bayesian modelsCode0
Show:102550
← PrevPage 57 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