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 3826–3850 of 10307 papers

TitleStatusHype
Evolutionary Algorithms in the Light of SGD: Limit Equivalence, Minima Flatness, and Transfer Learning—0
Evolutionary Algorithms Approach For Search Based On Semantic Document Similarity—0
CLIP-S^4: Language-Guided Self-Supervised Semantic Segmentation—0
A QUBO Framework for Team Formation—0
Evidence-empowered Transfer Learning for Alzheimer's Disease—0
Everything old is new again: A multi-view learning approach to learning using privileged information and distillation—0
Exponential Moving Average of Weights in Deep Learning: Dynamics and Benefits—0
Exposing Computer Generated Images by Using Deep Convolutional Neural Networks—0
Exposing the limits of Zero-shot Cross-lingual Hate Speech Detection—0
Everything is a Video: Unifying Modalities through Next-Frame Prediction—0
Event USKT : U-State Space Model in Knowledge Transfer for Event Cameras—0
CLIP is Almost All You Need: Towards Parameter-Efficient Scene Text Retrieval without OCR—0
Extending Multilingual BERT to Low-Resource Languages—0
A Quantum Neural Network Transfer-Learning Model for Forecasting Problems with Continuous and Discrete Variables—0
A fast general thermal simulation model based on MultiBranch Physics-Informed deep operator neural network—0
Active Reinforcement Learning -- A Roadmap Towards Curious Classifier Systems for Self-Adaptation—0
Event Extraction in Basque: Typologically motivated Cross-Lingual Transfer-Learning Analysis—0
EventDance: Unsupervised Source-free Cross-modal Adaptation for Event-based Object Recognition—0
Extracting Events from Industrial Incident Reports—0
Extracting Pasture Phenotype and Biomass Percentages using Weakly Supervised Multi-target Deep Learning on a Small Dataset—0
Event Camera Data Pre-training—0
Event Camera Data Dense Pre-training—0
Extracurricular Learning: Knowledge Transfer Beyond Empirical Distribution—0
Extreme Low Resolution Activity Recognition with Confident Spatial-Temporal Attention Transfer—0
CLIP-FLow: Contrastive Learning by semi-supervised Iterative Pseudo labeling for Optical Flow Estimation—0
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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