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 9876–9900 of 10307 papers

TitleStatusHype
Hashtag Healthcare: From Tweets to Mental Health Journals Using Deep Transfer Learning—0
Source-Target Similarity Modelings for Multi-Source Transfer Gaussian Process Regression—0
Deep Asymmetric Multi-task Feature LearningCode0
The HIT-SCIR System for End-to-End Parsing of Universal Dependencies—0
A Transition-based System for Universal Dependency Parsing—0
Multilingual Semantic Parsing And Code-SwitchingCode0
A Dataset for Sanskrit Word Segmentation—0
UParse: the Edinburgh system for the CoNLL 2017 UD shared task—0
Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasmCode0
Training Data Augmentation for Low-Resource Morphological Inflection—0
Transfer Learning with Label Noise—0
Regularization techniques for fine-tuning in neural machine translation—0
Spatial-Aware Object Embeddings for Zero-Shot Localization and Classification of Actions—0
Learning to Teach Reinforcement Learning Agents—0
Multi-Robot Transfer Learning: A Dynamical System Perspective—0
Exploiting Web Images for Weakly Supervised Object Detection—0
A Novel Transfer Learning Approach upon Hindi, Arabic, and Bangla Numerals using Convolutional Neural Networks—0
Spatiotemporal Modeling for Crowd Counting in Videos—0
Partial Transfer Learning with Selective Adversarial Networks—0
Mutual Alignment Transfer Learning—0
Boosted Zero-Shot Learning with Semantic Correlation Regularization—0
Progressive Joint Modeling in Unsupervised Single-channel Overlapped Speech Recognition—0
Decoupled classifiers for fair and efficient machine learning—0
Exploiting Convolutional Representations for Multiscale Human Settlement Detection—0
Learning to select data for transfer learning with Bayesian OptimizationCode0
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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