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

Showing 22762300 of 10307 papers

TitleStatusHype
Generalization Through The Lens Of Leave-One-Out ErrorCode0
General solutions for nonlinear differential equations: a rule-based self-learning approach using deep reinforcement learningCode0
GenSF: Simultaneous Adaptation of Generative Pre-trained Models and Slot FillingCode0
Gated Domain Units for Multi-source Domain GeneralizationCode0
Context selectivity with dynamic availability enables lifelong continual learningCode0
GAN Cocktail: mixing GANs without dataset accessCode0
An Analysis of the Influence of Transfer Learning When Measuring the Tortuosity of Blood VesselsCode0
GAN pretraining for deep convolutional autoencoders applied to Software-based Fingerprint Presentation Attack DetectionCode0
ADD: Frequency Attention and Multi-View based Knowledge Distillation to Detect Low-Quality Compressed Deepfake ImagesCode0
Gammatonegram Representation for End-to-End Dysarthric Speech Processing Tasks: Speech Recognition, Speaker Identification, and Intelligibility AssessmentCode0
GANTL: Towards Practical and Real-Time Topology Optimization with Conditional GANs and Transfer LearningCode0
Funnelling: A New Ensemble Method for Heterogeneous Transfer Learning and its Application to Cross-Lingual Text ClassificationCode0
FUSE-ing Language Models: Zero-Shot Adapter Discovery for Prompt Optimization Across TokenizersCode0
FuCiTNet: Improving the generalization of deep learning networks by the fusion of learned class-inherent transformationsCode0
Functional Knowledge Transfer with Self-supervised Representation LearningCode0
FUSE: Label-Free Image-Event Joint Monocular Depth Estimation via Frequency-Decoupled Alignment and Degradation-Robust FusionCode0
FTA-FTL: A Fine-Tuned Aggregation Federated Transfer Learning Scheme for Lithology Microscopic Image ClassificationCode0
Entity Tracking via Effective Use of Multi-Task Learning Model and Mention-guided DecodingCode0
FTL: Transfer Learning Nonlinear Plasma Dynamic Transitions in Low Dimensional Embeddings via Deep Neural NetworksCode0
Fuzzy Rank-based Fusion of CNN Models using Gompertz Function for Screening COVID-19 CT-ScansCode0
A Web-based Mpox Skin Lesion Detection System Using State-of-the-art Deep Learning Models Considering Racial DiversityCode0
A Wander Through the Multimodal Landscape: Efficient Transfer Learning via Low-rank Sequence Multimodal AdapterCode0
AdaTriplet-RA: Domain Matching via Adaptive Triplet and Reinforced Attention for Unsupervised Domain AdaptationCode0
From Patch to Image Segmentation using Fully Convolutional Networks -- Application to Retinal ImagesCode0
From Video Game to Real Robot: The Transfer between Action SpacesCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1APCLIPAccuracy84.2Unverified
2DFA-ENTAccuracy69.2Unverified
3DFA-SAFNAccuracy69.1Unverified
4EasyTLAccuracy63.3Unverified
5MEDAAccuracy60.3Unverified
#ModelMetricClaimedVerifiedStatus
1CNN10-20% Mask PSNR3.23Unverified
#ModelMetricClaimedVerifiedStatus
1Chatterjee, Dutta et al.[1]Accuracy96.12Unverified
#ModelMetricClaimedVerifiedStatus
1Co-TuningAccuracy85.65Unverified
#ModelMetricClaimedVerifiedStatus
1Physical AccessEER5.74Unverified
#ModelMetricClaimedVerifiedStatus
1riadd.aucmediAUROC0.95Unverified