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 2551–2575 of 10307 papers

TitleStatusHype
Language Representation Projection: Can We Transfer Factual Knowledge across Languages in Multilingual Language Models?—0
Improved Child Text-to-Speech Synthesis through Fastpitch-based Transfer LearningCode1
Sparse Contrastive Learning of Sentence Embeddings—0
Topology Only Pre-Training: Towards Generalised Multi-Domain Graph ModelsCode0
Mini but Mighty: Finetuning ViTs with Mini AdaptersCode1
Elastic Information Bottleneck—0
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
Machine Learning-Based Tea Leaf Disease Detection: A Comprehensive Review—0
Risk of Transfer Learning and its Applications in Finance—0
Quantifying the value of information transfer in population-based SHM—0
Understanding Deep Representation Learning via Layerwise Feature Compression and DiscriminationCode0
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
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
Vicinal Risk Minimization for Few-Shot Cross-lingual Transfer in Abusive Language Detection—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
M&M3D: Multi-Dataset Training and Efficient Network for Multi-view 3D Object DetectionCode0
IndoToD: A Multi-Domain Indonesian Benchmark For End-to-End Task-Oriented Dialogue SystemsCode0
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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