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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5526–5550 of 10307 papers

TitleStatusHype
Semi-supervised Chinese Word Segmentation for CLP2012—0
GSDA: Generative Adversarial Network-based Semi-Supervised Data Augmentation for Ultrasound Image Classification—0
VFed-SSD: Towards Practical Vertical Federated Advertising—0
Semi-supervised Domain Adaptation in Graph Transfer Learning—0
Semi-Supervised Histology Classification using Deep Multiple Instance Learning and Contrastive Predictive Coding—0
Semi-Supervised Learning Approach to Discover Enterprise User Insights from Feedback and Support—0
Semi-supervised Learning of Naive Bayes Classifier with feature constraints—0
Semi-supervised Learning using Denoising Autoencoders for Brain Lesion Detection and Segmentation—0
Semi-Supervised Lifelong Language Learning—0
Semi-supervised Object Detection: A Survey on Recent Research and Progress—0
Semi-supervised Regression Analysis with Model Misspecification and High-dimensional Data—0
Semi-supervised transfer learning for language expansion of end-to-end speech recognition models to low-resource languages—0
Semi-Supervising Learning, Transfer Learning, and Knowledge Distillation with SimCLR—0
Sense and Learn: Self-Supervision for Omnipresent Sensors—0
Sense representations for Portuguese: experiments with sense embeddings and deep neural language models—0
Sensing Urban Land-Use Patterns By Integrating Google Tensorflow And Scene-Classification Models—0
Training Large Scale Polynomial CNNs for E2E Inference over Homomorphic Encryption—0
Sensor2Text: Enabling Natural Language Interactions for Daily Activity Tracking Using Wearable Sensors—0
Sensor Transfer: Learning Optimal Sensor Effect Image Augmentation for Sim-to-Real Domain Adaptation—0
Sentence encoding for Dialogue Act classification—0
Sentence-Level Propaganda Detection in News Articles with Transfer Learning and BERT-BiLSTM-Capsule Model—0
Sentiment Analysis and Sarcasm Detection of Indian General Election Tweets—0
Sentiment Analysis for Hinglish Code-mixed Tweets by means of Cross-lingual Word Embeddings—0
Sentiment Relevance—0
Separated Contrastive Learning for Matching in Cross-domain Recommendation with Curriculum Scheduling—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