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 1401–1450 of 10307 papers

TitleStatusHype
PP-TIL: Personalized Planning for Autonomous Driving with Instance-based Transfer Imitation LearningCode1
GLAM: Glomeruli Segmentation for Human Pathological Lesions using Adapted Mouse Model—0
Advancing 3D Point Cloud Understanding through Deep Transfer Learning: A Comprehensive Survey—0
How Lightweight Can A Vision Transformer Be—0
Exploring the Effect of Dataset Diversity in Self-Supervised Learning for Surgical Computer VisionCode2
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
Detection of manatee vocalisations using the Audio Spectrogram TransformerCode0
Peak-Controlled Logits Poisoning Attack in Federated Distillation—0
Forecasting Credit Ratings: A Case Study where Traditional Methods Outperform Generative LLMsCode0
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
Federated Automatic Latent Variable Selection in Multi-output Gaussian Processes—0
Towards scalable efficient on-device ASR with transfer learning—0
AbdomenAtlas: A Large-Scale, Detailed-Annotated, & Multi-Center Dataset for Efficient Transfer Learning and Open Algorithmic BenchmarkingCode3
Exploring the Effectiveness and Consistency of Task Selection in Intermediate-Task Transfer LearningCode0
ODGR: Online Dynamic Goal Recognition—0
EffiSegNet: Gastrointestinal Polyp Segmentation through a Pre-Trained EfficientNet-based Network with a Simplified DecoderCode1
Generalizing Teacher Networks for Effective Knowledge Distillation Across Student ArchitecturesCode0
Affordance Labeling and Exploration: A Manifold-Based Approach—0
ALLaM: Large Language Models for Arabic and English—0
TreeSBA: Tree-Transformer for Self-Supervised Sequential Brick AssemblyCode0
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
Enhancing Wildfire Forecasting Through Multisource Spatio-Temporal Data, Deep Learning, Ensemble Models and Transfer Learning—0
Enhancing Skin Disease Classification Leveraging Transformer-based Deep Learning Architectures and Explainable AI—0
Riemannian Geometry-Based EEG Approaches: A Literature Review—0
A Comparative Study of Transfer Learning for Emotion Recognition using CNN and Modified VGG16 Models—0
Vision-Based Power Line Cables and Pylons Detection for Low Flying Aircraft—0
An Attention-based Representation Distillation Baseline for Multi-Label Continual LearningCode0
Quantifying the value of positive transfer: An experimental case study—0
Dyn-Adapter: Towards Disentangled Representation for Efficient Visual Recognition—0
Straightforward Layer-wise Pruning for More Efficient Visual AdaptationCode0
Rethinking Visual Content Refinement in Low-Shot CLIP AdaptationCode1
Enhancing Graph Neural Networks with Limited Labeled Data by Actively Distilling Knowledge from Large Language Models—0
Semi-Supervised Contrastive Learning of Musical RepresentationsCode1
Are We Ready for Out-of-Distribution Detection in Digital Pathology?—0
MO-EMT-NAS: Multi-Objective Continuous Transfer of Architectural Knowledge Between Tasks from Different Datasets—0
A Scalable and Generalized Deep Learning Framework for Anomaly Detection in Surveillance Videos—0
On Initializing Transformers with Pre-trained Embeddings—0
Green Resource Allocation in Cloud-Native O-RAN Enabled Small Cell Networks—0
Exploring connections of spectral analysis and transfer learning in medical imaging—0
Encapsulating Knowledge in One PromptCode1
MRIo3DS-Net: A Mutually Reinforcing Images to 3D Surface RNN-like framework for model-adaptation indoor 3D reconstruction—0
Novel Artistic Scene-Centric Datasets for Effective Transfer Learning in Fragrant Spaces—0
Genomic Language Models: Opportunities and Challenges—0
LoRA-PT: Low-Rank Adapting UNETR for Hippocampus Segmentation Using Principal Tensor Singular Values and VectorsCode0
Relational Representation DistillationCode1
MapDistill: Boosting Efficient Camera-based HD Map Construction via Camera-LiDAR Fusion Model Distillation—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