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 1976–2000 of 10307 papers

TitleStatusHype
Graph-Sequential Alignment and Uniformity: Toward Enhanced Recommendation SystemsCode0
Manifold Characteristics That Predict Downstream Task PerformanceCode0
Manifold Criterion Guided Transfer Learning via Intermediate Domain GenerationCode0
GreekBART: The First Pretrained Greek Sequence-to-Sequence ModelCode0
Building an Endangered Language Resource in the Classroom: Universal Dependencies for KakataiboCode0
Growing Neural Network with Shared ParameterCode0
Complete CVDL Methodology for Investigating Hydrodynamic InstabilitiesCode0
ANNA: Abstractive Text-to-Image Synthesis with Filtered News CaptionsCode0
An LSTM Feature Imitation Network for Hand Movement Recognition from sEMG SignalsCode0
Foundation-Model-Boosted Multimodal Learning for fMRI-based Neuropathic Pain Drug Response PredictionCode0
AniWho : A Quick and Accurate Way to Classify Anime Character Faces in ImagesCode0
Bringing Cartoons to Life: Towards Improved Cartoon Face Detection and Recognition SystemsCode0
Graph Few-shot Learning via Knowledge TransferCode0
Advanced Knowledge Transfer: Refined Feature Distillation for Zero-Shot Quantization in Edge ComputingCode0
Graph Neural Networks for Surfactant Multi-Property PredictionCode0
Graph Constrained Data Representation Learning for Human Motion SegmentationCode0
GraphBridge: Towards Arbitrary Transfer Learning in GNNsCode0
Bridging the gap between Natural and Medical Images through Deep ColorizationCode0
Graph-based Knowledge Distillation by Multi-head Attention NetworkCode0
Graph Distillation for Action Detection with Privileged ModalitiesCode0
An Iterative Multi-Knowledge Transfer Network for Aspect-Based Sentiment AnalysisCode0
Grad2Task: Improved Few-shot Text Classification Using Gradients for Task RepresentationCode0
Absolute Zero-Shot LearningCode0
GPT-3 Models are Poor Few-Shot Learners in the Biomedical DomainCode0
Hardware-accelerated Mars Sample Localization via deep transfer learning from photorealistic simulationsCode0
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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