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 76–100 of 10307 papers

TitleStatusHype
Accessing Vision Foundation Models at ImageNet-level CostsCode2
Efficient Remote Sensing with Harmonized Transfer Learning and Modality AlignmentCode2
ExpeL: LLM Agents Are Experiential LearnersCode2
Dynamic Adapter Meets Prompt Tuning: Parameter-Efficient Transfer Learning for Point Cloud AnalysisCode2
DinoBloom: A Foundation Model for Generalizable Cell Embeddings in HematologyCode2
Deep Model ReassemblyCode2
Deep learning for time series classificationCode2
Deep Neural Networks to Detect Weeds from Crops in Agricultural Environments in Real-Time: A ReviewCode2
AddressCLIP: Empowering Vision-Language Models for City-wide Image Address LocalizationCode2
Discovery of 2D materials using Transformer Network based Generative DesignCode2
Do MIL Models Transfer?Code2
Deep Learning-Enabled Semantic Communication Systems with Task-Unaware Transmitter and Dynamic DataCode2
Densely Connected Parameter-Efficient Tuning for Referring Image SegmentationCode2
Global birdsong embeddings enable superior transfer learning for bioacoustic classificationCode2
Enhancing Zero-Shot Facial Expression Recognition by LLM Knowledge TransferCode2
3D UX-Net: A Large Kernel Volumetric ConvNet Modernizing Hierarchical Transformer for Medical Image SegmentationCode2
Exploring the Effect of Dataset Diversity in Self-Supervised Learning for Surgical Computer VisionCode2
Spatio-Temporal Few-Shot Learning via Diffusive Neural Network GenerationCode2
Content-Based Search for Deep Generative ModelsCode2
An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated LearningCode2
AdapterFusion: Non-Destructive Task Composition for Transfer LearningCode2
CommonCanvas: An Open Diffusion Model Trained with Creative-Commons ImagesCode2
All-in-one foundational models learning across quantum chemical levelsCode2
Foundation Model for Endoscopy Video Analysis via Large-scale Self-supervised Pre-trainCode2
Constructing and Exploring Intermediate Domains in Mixed Domain Semi-supervised Medical Image SegmentationCode2
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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