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 7551–7575 of 10307 papers

TitleStatusHype
Voting-based Approaches For Differentially Private Federated Learning—0
Weaponizing Unicodes with Deep Learning -- Identifying Homoglyphs with Weakly Labeled DataCode0
Towards the Detection of Building Occupancy with Synthetic Environmental Data—0
Transfer Learning and SpecAugment applied to SSVEP Based BCI Classification—0
Learning to Evaluate Translation Beyond English: BLEURT Submissions to the WMT Metrics 2020 Shared Task—0
Leveraging Unpaired Text Data for Training End-to-End Speech-to-Intent Systems—0
Don't Parse, Insert: Multilingual Semantic Parsing with Insertion Based Decoding—0
An Audio-Video Deep and Transfer Learning Framework for Multimodal Emotion Recognition in the wild—0
Explanation and Use of Uncertainty Quantified by Bayesian Neural Network Classifiers for Breast Histopathology Images—0
Conversion and Implementation of State-of-the-Art Deep Learning Algorithms for the Classification of Diabetic Retinopathy—0
AxFormer: Accuracy-driven Approximation of Transformers for Faster, Smaller and more Accurate NLP ModelsCode0
Cognitive Learning-Aided Multi-Antenna Communications—0
"I'd rather just go to bed": Understanding Indirect Answers—0
Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on African LanguagesCode0
Variational Feature Disentangling for Fine-Grained Few-Shot Classification—0
Contrastive Cross-Modal Pre-Training: A General Strategy for Small Sample Medical Imaging—0
Domain Adaptive Transfer Learning on Visual Attention Aware Data Augmentation for Fine-grained Visual Categorization—0
Learning Diverse Options via InfoMax Termination CriticCode0
Multi-task Learning for Multilingual Neural Machine Translation—0
RANDGAN: Randomized Generative Adversarial Network for Detection of COVID-19 in Chest X-rayCode0
DaNetQA: a yes/no Question Answering Dataset for the Russian Language—0
Representation learning from videos in-the-wild: An object-centric approach—0
LEAPME: Learning-based Property Matching with Embeddings—0
Generating Gameplay-Relevant Art Assets with Transfer LearningCode0
A New Mask R-CNN Based Method for Improved Landslide 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