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 1151–1175 of 10307 papers

TitleStatusHype
Adaptive Consistency Regularization for Semi-Supervised Transfer LearningCode1
CheXWorld: Exploring Image World Modeling for Radiograph Representation LearningCode1
HETAL: Efficient Privacy-preserving Transfer Learning with Homomorphic EncryptionCode1
Heterogeneous Graph Contrastive Learning for RecommendationCode1
AutoInit: Analytic Signal-Preserving Weight Initialization for Neural NetworksCode1
Hi-End-MAE: Hierarchical encoder-driven masked autoencoders are stronger vision learners for medical image segmentationCode1
High-Modality Multimodal Transformer: Quantifying Modality & Interaction Heterogeneity for High-Modality Representation LearningCode1
CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer LearningCode1
CEM500K – A large-scale heterogeneous unlabeled cellular electron microscopy image dataset for deep learningCode1
Authorship Style Transfer with Policy OptimizationCode1
AutoKE: An automatic knowledge embedding framework for scientific machine learningCode1
CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly LocalizationCode1
Exploring the Transfer Learning Capabilities of CLIP in Domain Generalization for Diabetic RetinopathyCode1
How Hateful are Movies? A Study and Prediction on Movie SubtitlesCode1
Exploring Transfer Learning for Low Resource Emotional TTSCode1
Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric ModelsCode1
ChrEn: Cherokee-English Machine Translation for Endangered Language RevitalizationCode1
Chip Placement with Deep Reinforcement LearningCode1
A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositionsCode1
ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft PromptsCode1
CIPS-3D: A 3D-Aware Generator of GANs Based on Conditionally-Independent Pixel SynthesisCode1
Hybrid thermal modeling of additive manufacturing processes using physics-informed neural networks for temperature prediction and parameter identificationCode1
Neural Architecture Search using Deep Neural Networks and Monte Carlo Tree SearchCode1
Attention-Based Deep Learning Framework for Human Activity Recognition with User AdaptationCode1
Neural Model Reprogramming with Similarity Based Mapping for Low-Resource Spoken Command RecognitionCode1
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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