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 2551–2600 of 10307 papers

TitleStatusHype
Improved Child Text-to-Speech Synthesis through Fastpitch-based Transfer LearningCode1
Sparse Contrastive Learning of Sentence Embeddings—0
Language Representation Projection: Can We Transfer Factual Knowledge across Languages in Multilingual Language Models?—0
Topology Only Pre-Training: Towards Generalised Multi-Domain Graph ModelsCode0
Elastic Information Bottleneck—0
Mini but Mighty: Finetuning ViTs with Mini AdaptersCode1
Supervised domain adaptation for building extraction from off-nadir aerial images—0
Mapping of Land Use and Land Cover (LULC) using EuroSAT and Transfer LearningCode0
Risk of Transfer Learning and its Applications in Finance—0
Machine Learning-Based Tea Leaf Disease Detection: A Comprehensive Review—0
Understanding Deep Representation Learning via Layerwise Feature Compression and DiscriminationCode0
Quantifying the value of information transfer in population-based SHM—0
TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML ApplicationsCode6
CDR-Adapter: Learning Adapters to Dig Out More Transferring Ability for Cross-Domain Recommendation Models—0
What Makes Pre-Trained Visual Representations Successful for Robust Manipulation?—0
Use of Deep Neural Networks for Uncertain Stress Functions with Extensions to Impact Mechanics—0
Determination of droplet size from wide-angle light scattering image data using convolutional neural networks—0
Robust Fine-Tuning of Vision-Language Models for Domain GeneralizationCode0
Vicinal Risk Minimization for Few-Shot Cross-lingual Transfer in Abusive Language Detection—0
CheX-Nomaly: Segmenting Lung Abnormalities from Chest Radiographs using Machine Learning—0
Capturing Local and Global Features in Medical Images by Using Ensemble CNN-Transformer—0
LOTUS: Continual Imitation Learning for Robot Manipulation Through Unsupervised Skill Discovery—0
Adversary ML Resilience in Autonomous Driving Through Human Centered Perception Mechanisms—0
Expanding Expressiveness of Diffusion Models with Limited Data via Self-Distillation based Fine-Tuning—0
M&M3D: Multi-Dataset Training and Efficient Network for Multi-view 3D Object DetectionCode0
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