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
Beamforming and Resource Allocation for Delay Optimization in RIS-Assisted OFDM Systems—0
TokAlign: Efficient Vocabulary Adaptation via Token AlignmentCode1
HtFLlib: A Comprehensive Heterogeneous Federated Learning Library and BenchmarkCode3
StARS DCM: A Sleep Stage-Decoding Forehead EEG Patch for Real-time Modulation of Sleep Physiology—0
Multi-Platform Methane Plume Detection via Model and Domain Adaptation—0
EfficientFER: EfficientNetv2 Based Deep Learning Approach for Facial Expression RecognitionCode1
TaxaDiffusion: Progressively Trained Diffusion Model for Fine-Grained Species GenerationCode0
Getting More from Less: Transfer Learning Improves Sleep Stage Decoding Accuracy in Peripheral Wearable Devices—0
Neuro2Semantic: A Transfer Learning Framework for Semantic Reconstruction of Continuous Language from Human Intracranial EEGCode1
COGNATE: Acceleration of Sparse Tensor Programs on Emerging Hardware using Transfer Learning—0
Lightweight Convolutional Neural Networks for Retinal Disease Classification—0
Progressive Class-level Distillation—0
Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image SegmentationCode0
Conformal Prediction for Zero-Shot ModelsCode1
LLMs Are Globally Multilingual Yet Locally Monolingual: Exploring Knowledge Transfer via Language and Thought Theory—0
Proactive Guidance of Multi-Turn Conversation in Industrial Search—0
Improving Language and Modality Transfer in Translation by Character-level Modeling—0
Attractor learning for spatiotemporally chaotic dynamical systems using echo state networks with transfer learning—0
Zero-Shot Adaptation of Parameter-Efficient Fine-Tuning in Diffusion Models—0
Personalized Subgraph Federated Learning with Differentiable Auxiliary Projections—0
BIRD: Behavior Induction via Representation-structure Distillation—0
To Trust Or Not To Trust Your Vision-Language Model's PredictionCode1
Epistemic Errors of Imperfect Multitask Learners When Distributions Shift—0
Graph Positional Autoencoders as Self-supervised Learners—0
FreRA: A Frequency-Refined Augmentation for Contrastive Learning on Time Series ClassificationCode1
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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