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 18261850 of 10307 papers

TitleStatusHype
Advancing Transformer's Capabilities in Commonsense ReasoningCode0
Catastrophic Forgetting Meets Negative Transfer: Batch Spectral Shrinkage for Safe Transfer LearningCode0
Histogram-based Parameter-efficient Tuning for Passive Sonar ClassificationCode0
HIT: A Hierarchically Fused Deep Attention Network for Robust Code-mixed Language RepresentationCode0
Hostility Detection in Hindi leveraging Pre-Trained Language ModelsCode0
Hindi/Bengali Sentiment Analysis Using Transfer Learning and Joint Dual Input Learning with Self AttentionCode0
Hi-gMISnet: generalized medical image segmentation using DWT based multilayer fusion and dual mode attention into high resolution pGANCode0
HintNet: Hierarchical Knowledge Transfer Networks for Traffic Accident Forecasting on Heterogeneous Spatio-Temporal DataCode0
Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource LanguagesCode0
Hierarchical transfer learning with applications for electricity load forecastingCode0
CLIP-based Synergistic Knowledge Transfer for Text-based Person RetrievalCode0
CARTL: Cooperative Adversarially-Robust Transfer LearningCode0
Hierarchical Topology Isomorphism Expertise Embedded Graph Contrastive LearningCode0
HOUDINI: Lifelong Learning as Program SynthesisCode0
CARL-D: A vision benchmark suite and large scale dataset for vehicle detection and scene segmentationCode0
Advancing Multilingual Handwritten Numeral Recognition with Attention-driven Transfer LearningCode0
Cardiac MRI Orientation Recognition and Standardization using Deep Neural NetworksCode0
Heterogeneous Transfer Learning for Building High-Dimensional Generalized Linear Models with Disparate DatasetsCode0
Knowledge transfer across cell lines using Hybrid Gaussian Process models with entity embedding vectorsCode0
Capturing Pertinent Symbolic Features for Enhanced Content-Based Misinformation DetectionCode0
CL-NERIL: A Cross-Lingual Model for NER in Indian LanguagesCode0
Heterogeneous Treatment Effect with Trained Kernels of the Nadaraya-Watson RegressionCode0
Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing ImageryCode0
Arabic Dialect Identification Using BERT Fine-TuningCode0
HierarchicalContrast: A Coarse-to-Fine Contrastive Learning Framework for Cross-Domain Zero-Shot Slot FillingCode0
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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