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
Exploring Transfer Learning for Urdu Speech Synthesis—0
Figure Eight at SemEval-2019 Task 3: Ensemble of Transfer Learning Methods for Contextual Emotion Detection—0
Automated Testing of Spatially-Dependent Environmental Hypotheses through Active Transfer Learning—0
A Compare-Aggregate Model with Latent Clustering for Answer Selection—0
Decoupled classifiers for fair and efficient machine learning—0
Exploring Vision Transformers for 3D Human Motion-Language Models with Motion Patches—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
Expressive Power of Randomized Signature—0
Decoupled Box Proposal and Featurization with Ultrafine-Grained Semantic Labels Improve Image Captioning and Visual Question Answering—0
Colorectal cancer diagnosis from histology images: A comparative study—0
Extending Multilingual BERT to Low-Resource Languages—0
A Multimodal German Dataset for Automatic Lip Reading Systems and Transfer Learning—0
Using LLMs to Establish Implicit User Sentiment of Software Desirability—0
Decomposition-Based Transfer Distance Metric Learning for Image Classification—0
External knowledge transfer deployment inside a simple double agent Viterbi algorithm—0
A Multi-media Approach to Cross-lingual Entity Knowledge Transfer—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
Combination of Domain Knowledge and Deep Learning for Sentiment Analysis of Short and Informal Messages on Social Media—0
Extraction of Key-frames of Endoscopic Videos by using Depth Information—0
Extracurricular Learning: Knowledge Transfer Beyond Empirical Distribution—0
Extreme Low Resolution Activity Recognition with Confident Spatial-Temporal Attention Transfer—0
Decomposed Cross-modal Distillation for RGB-based Temporal Action Detection—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