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 2576–2600 of 10307 papers

TitleStatusHype
Scattering Vision Transformer: Spectral Mixing Matters—0
IndoToD: A Multi-Domain Indonesian Benchmark For End-to-End Task-Oriented Dialogue SystemsCode0
TLMCM Network for Medical Image Hierarchical Multi-Label Classification—0
Transfer learning for improved generalizability in causal physics-informed neural networks for beam simulations—0
ZEETAD: Adapting Pretrained Vision-Language Model for Zero-Shot End-to-End Temporal Action Detection—0
Graph Neural Networks for Road Safety Modeling: Datasets and Evaluations for Accident AnalysisCode1
Investigating Relative Performance of Transfer and Meta Learning—0
Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning—0
DDAM-PS: Diligent Domain Adaptive Mixer for Person SearchCode1
Self-Supervised Pre-Training for Precipitation Post-Processor—0
MENTOR: Human Perception-Guided Pretraining for Increased Generalization—0
On consequences of finetuning on data with highly discriminative features—0
Promise:Prompt-driven 3D Medical Image Segmentation Using Pretrained Image Foundation ModelsCode1
AMLNet: Adversarial Mutual Learning Neural Network for Non-AutoRegressive Multi-Horizon Time Series ForecastingCode0
Topological Learning for Motion Data via Mixed CoordinatesCode0
CreoleVal: Multilingual Multitask Benchmarks for CreolesCode1
Label-Only Model Inversion Attacks via Knowledge TransferCode1
Adapter Pruning using Tropical Characterization—0
RCKD: Response-Based Cross-Task Knowledge Distillation for Pathological Image Analysis—0
A foundational neural operator that continuously learns without forgetting—0
BirdSAT: Cross-View Contrastive Masked Autoencoders for Bird Species Classification and MappingCode1
Automaton Distillation: Neuro-Symbolic Transfer Learning for Deep Reinforcement Learning—0
QWID: Quantized Weed Identification Deep neural networkCode0
A transfer learning approach with convolutional neural network for Face Mask Detection—0
Transfer Learning in Transformer-Based Demand Forecasting For Home Energy Management System—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