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 8901–8925 of 10307 papers

TitleStatusHype
Utilizing Deep Learning Towards Multi-modal Bio-sensing and Vision-based Affective Computing—0
Improving Relevance Prediction with Transfer Learning in Large-scale Retrieval Systems—0
SuperChat: Dialogue Generation by Transfer Learning from Vision to Language using Two-dimensional Word Embedding and Pretrained ImageNet CNN Models—0
Learning Visually Consistent Label Embeddings for Zero-Shot Learning—0
System Demo for Transfer Learning from Vision to Language using Domain Specific CNN Accelerator for On-Device NLP Applications—0
Fonts-2-Handwriting: A Seed-Augment-Train framework for universal digit classificationCode0
Active Multitask Learning with Committees—0
Enabling Continual Learning in Neural Networks with Meta Learning—0
Domain Adaptive Transfer Learning for Fault Diagnosis—0
Learning What and Where to TransferCode0
Transferable Clean-Label Poisoning Attacks on Deep Neural NetsCode0
Effective Cross-lingual Transfer of Neural Machine Translation Models without Shared VocabulariesCode0
A Brief Survey of Multilingual Neural Machine Translation—0
Transfer Learning for Scientific Data Chain Extraction in Small Chemical Corpus with BERT-CRF Model—0
Data augmentation in microscopic images for material data mining—0
Deep Zero-Shot Learning for Scene Sketch—0
A logical-based corpus for cross-lingual evaluationCode0
Anomaly Detection in Images—0
SuperChat: Dialogue Generation by Transfer Learning from Vision to Language using Two-dimensional Word Embedding and Pretrained ImageNet CNN Models—0
Reinforced Genetic Algorithm Learning for Optimizing Computation Graphs—0
Transferable Knowledge for Low-cost Decision Making in Cloud Environments—0
An Empirical Evaluation of Adversarial Robustness under Transfer Learning—0
Tuned Inception V3 for Recognizing States of Cooking Ingredients—0
On Transfer Learning For Chatter Detection in Turning Using Wavelet Packet Transform and Empirical Mode DecompositionCode0
From Video Game to Real Robot: The Transfer between Action SpacesCode0
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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