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 551–575 of 10307 papers

TitleStatusHype
Decoupled Multimodal Distilling for Emotion RecognitionCode1
An Efficient Knowledge Transfer Strategy for Spiking Neural Networks from Static to Event DomainCode1
Contrastive Alignment of Vision to Language Through Parameter-Efficient Transfer LearningCode1
Fix the Noise: Disentangling Source Feature for Controllable Domain TranslationCode1
GeoMIM: Towards Better 3D Knowledge Transfer via Masked Image Modeling for Multi-view 3D UnderstandingCode1
Partial Network CloningCode1
PFSL: Personalized & Fair Split Learning with Data & Label Privacy for thin clientsCode1
AdaptGuard: Defending Against Universal Attacks for Model AdaptationCode1
Trainable Projected Gradient Method for Robust Fine-tuningCode1
Denoising Diffusion Autoencoders are Unified Self-supervised LearnersCode1
Deep Metric Learning for Unsupervised Remote Sensing Change DetectionCode1
DAA: A Delta Age AdaIN operation for age estimation via binary code transformerCode1
Scaling Up 3D Kernels with Bayesian Frequency Re-parameterization for Medical Image SegmentationCode1
HiNet: Novel Multi-Scenario & Multi-Task Learning with Hierarchical Information ExtractionCode1
MixSpeech: Cross-Modality Self-Learning with Audio-Visual Stream Mixup for Visual Speech Translation and RecognitionCode1
Imbalanced Open Set Domain Adaptation via Moving-threshold Estimation and Gradual AlignmentCode1
Multimodal Parameter-Efficient Few-Shot Class Incremental LearningCode1
CUDA: Convolution-based Unlearnable DatasetsCode1
Heterogeneous Graph Contrastive Learning for RecommendationCode1
Evaluating Parameter-Efficient Transfer Learning Approaches on SURE Benchmark for Speech UnderstandingCode1
Distillation from Heterogeneous Models for Top-K RecommendationCode1
Pay Less But Get More: A Dual-Attention-based Channel Estimation Network for Massive MIMO Systems with Low-Density PilotsCode1
A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositionsCode1
You Only Transfer What You Share: Intersection-Induced Graph Transfer Learning for Link PredictionCode1
The Role of Pre-training Data in Transfer LearningCode1
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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