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 326–350 of 10307 papers

TitleStatusHype
Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated ExpertsCode1
Fine-grained Prompt Tuning: A Parameter and Memory Efficient Transfer Learning Method for High-resolution Medical Image ClassificationCode1
Authorship Style Transfer with Policy OptimizationCode1
Can LLMs' Tuning Methods Work in Medical Multimodal Domain?Code1
Frequency Attention for Knowledge DistillationCode1
RadarDistill: Boosting Radar-based Object Detection Performance via Knowledge Distillation from LiDAR FeaturesCode1
OmniJet-α: The first cross-task foundation model for particle physicsCode1
LEAD: Learning Decomposition for Source-free Universal Domain AdaptationCode1
A Unified Framework for Microscopy Defocus Deblur with Multi-Pyramid Transformer and Contrastive LearningCode1
COLA: Cross-city Mobility Transformer for Human Trajectory SimulationCode1
On Latency Predictors for Neural Architecture SearchCode1
Diffusion-Based Neural Network Weights GenerationCode1
MedContext: Learning Contextual Cues for Efficient Volumetric Medical SegmentationCode1
NocPlace: Nocturnal Visual Place Recognition via Generative and Inherited Knowledge TransferCode1
LEIA: Facilitating Cross-lingual Knowledge Transfer in Language Models with Entity-based Data AugmentationCode1
ZeroG: Investigating Cross-dataset Zero-shot Transferability in GraphsCode1
BrainWave: A Brain Signal Foundation Model for Clinical ApplicationsCode1
FedLPS: Heterogeneous Federated Learning for Multiple Tasks with Local Parameter SharingCode1
A Competition Winning Deep Reinforcement Learning Agent in microRTSCode1
MODIPHY: Multimodal Obscured Detection for IoT using PHantom Convolution-Enabled Faster YOLOCode1
Group Distributionally Robust Dataset Distillation with Risk MinimizationCode1
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation ModelsCode1
Pruner: A Speculative Exploration Mechanism to Accelerate Tensor Program TuningCode1
Deep Semantic-Visual Alignment for Zero-Shot Remote Sensing Image Scene ClassificationCode1
Efficient Fine-tuning of Audio Spectrogram Transformers via Soft Mixture of AdaptersCode1
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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