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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3601–3625 of 10307 papers

TitleStatusHype
TAKT: Target-Aware Knowledge Transfer for Whole Slide Image ClassificationCode0
HiNet: Novel Multi-Scenario & Multi-Task Learning with Hierarchical Information ExtractionCode1
Weakly Supervised Knowledge Transfer with Probabilistic Logical Reasoning for Object DetectionCode0
MixSpeech: Cross-Modality Self-Learning with Audio-Visual Stream Mixup for Visual Speech Translation and RecognitionCode1
KubeEdge-Sedna v0.3: Towards Next-Generation Automatically Customized AI Engineering Scheme—0
Imbalanced Open Set Domain Adaptation via Moving-threshold Estimation and Gradual AlignmentCode1
Multimodal Parameter-Efficient Few-Shot Class Incremental LearningCode1
Computing with Categories in Machine Learning—0
Leveraging Pre-trained AudioLDM for Sound Generation: A Benchmark Study—0
Your representations are in the network: composable and parallel adaptation for large scale models—0
CUDA: Convolution-based Unlearnable DatasetsCode1
PSDNet: Determination of Particle Size Distributions Using Synthetic Soil Images and Convolutional Neural Networks—0
A Comparison of Methods for Neural Network Aggregation—0
Multitask Prompt Tuning Enables Parameter-Efficient Transfer Learning—0
Cross-Lingual Transfer Learning for Alzheimer's Detection From Spontaneous Speech—0
Environment Invariant Linear Least SquaresCode0
To Stay or Not to Stay in the Pre-train Basin: Insights on Ensembling in Transfer LearningCode0
Training-Free Acceleration of ViTs with Delayed Spatial MergingCode0
RePreM: Representation Pre-training with Masked Model for Reinforcement Learning—0
Exploring Self-Supervised Representation Learning For Low-Resource Medical Image AnalysisCode0
Cross-domain Transfer Learning and State Inference for Soft Robots via a Semi-supervised Sequential Variational Bayes FrameworkCode0
Evaluating Parameter-Efficient Transfer Learning Approaches on SURE Benchmark for Speech UnderstandingCode1
Artificial Intelligence for Dementia Research Methods Optimization—0
Pay Less But Get More: A Dual-Attention-based Channel Estimation Network for Massive MIMO Systems with Low-Density PilotsCode1
Evidence-empowered Transfer Learning for Alzheimer's Disease—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