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 1401–1425 of 10307 papers

TitleStatusHype
Diffusion-Based Neural Network Weights GenerationCode1
Open-Pose 3D Zero-Shot Learning: Benchmark and ChallengesCode1
Diffusion Models Beat GANs on Image ClassificationCode1
ASSET: Robust Backdoor Data Detection Across a Multiplicity of Deep Learning ParadigmsCode1
Google Landmarks Dataset v2 - A Large-Scale Benchmark for Instance-Level Recognition and RetrievalCode1
PaStaNet: Toward Human Activity Knowledge EngineCode1
Golos: Russian Dataset for Speech ResearchCode1
Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent AlignmentCode1
Bayesian Optimization with Automatic Prior Selection for Data-Efficient Direct Policy SearchCode1
Efficient Conditional GAN Transfer with Knowledge Propagation across ClassesCode1
Disentangled Knowledge Transfer for OOD Intent Discovery with Unified Contrastive LearningCode1
Disentangled Pre-training for Human-Object Interaction DetectionCode1
Disentangling Spatial and Temporal Learning for Efficient Image-to-Video Transfer LearningCode1
Bert4XMR: Cross-Market Recommendation with Bidirectional Encoder Representations from TransformerCode1
DistillBEV: Boosting Multi-Camera 3D Object Detection with Cross-Modal Knowledge DistillationCode1
Distillation from Heterogeneous Models for Top-K RecommendationCode1
PINNACLE: PINN Adaptive ColLocation and Experimental points selectionCode1
Distilling BlackBox to Interpretable models for Efficient Transfer LearningCode1
Distilling Image Classifiers in Object DetectorsCode1
Google Landmarks Dataset v2 -- A Large-Scale Benchmark for Instance-Level Recognition and RetrievalCode1
Distilling Knowledge via Intermediate ClassifiersCode1
PointCLIP: Point Cloud Understanding by CLIPCode1
A Comprehensive Study on Torchvision Pre-trained Models for Fine-grained Inter-species ClassificationCode1
Distribution-aware Knowledge Prototyping for Non-exemplar Lifelong Person Re-identificationCode1
GPPT: Graph Pre-training and Prompt Tuning to Generalize Graph Neural NetworksCode1
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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