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 9201–9250 of 10307 papers

TitleStatusHype
Unsupervised Transfer Learning for Spoken Language Understanding in Intelligent AgentsCode0
A Framework of Transfer Learning in Object Detection for Embedded SystemsCode0
Improving speech emotion recognition via Transformer-based Predictive Coding through transfer learning—0
A Fully Automated System for Sizing Nasal PAP Masks Using Facial Photographs—0
M2M-GAN: Many-to-Many Generative Adversarial Transfer Learning for Person Re-Identification—0
Doc2Im: document to image conversion through self-attentive embedding—0
Knowledge Transfer via Distillation of Activation Boundaries Formed by Hidden NeuronsCode0
Analysis of Multilingual Sequence-to-Sequence speech recognition systems—0
Baselines for Reinforcement Learning in Text Games—0
Transfer Learning from LDA to BiLSTM-CNN for Offensive Language Detection in Twitter—0
Language model integration based on memory control for sequence to sequence speech recognition—0
Revealing Fine Structures of the Retinal Receptive Field by Deep Learning Networks—0
Micro-Attention for Micro-Expression recognitionCode0
Transfer learning of language-independent end-to-end ASR with language model fusion—0
Transfer learning for time series classificationCode1
Transfer Learning in Multilingual Neural Machine Translation with Dynamic VocabularyCode1
Zero-Shot Transfer VQA Dataset—0
Learning to Rank Query Graphs for Complex Question Answering over Knowledge GraphsCode0
Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data TasksCode0
Low-resource named entity recognition via multi-source projection: Not quite there yet?—0
Cross-Lingual Argumentative Relation Identification: from English to PortugueseCode0
Language Modeling Teaches You More than Translation Does: Lessons Learned Through Auxiliary Syntactic Task Analysis—0
An Analysis of Encoder Representations in Transformer-Based Machine Translation—0
DERE: A Task and Domain-Independent Slot Filling Framework for Declarative Relation Extraction—0
Universal Sentence Encoder for English—0
Learning Unsupervised Word Mapping by Maximizing Mean Discrepancy—0
Addressing word-order Divergence in Multilingual Neural Machine Translation for extremely Low Resource Languages—0
Cogni-Net: Cognitive Feature Learning through Deep Visual PerceptionCode0
Multilingual NMT with a language-independent attention bridgeCode0
Don't forget, there is more than forgetting: new metrics for Continual Learning—0
Cross-Lingual Transfer Learning for Multilingual Task Oriented Dialog—0
Adaptive Transfer Learning in Deep Neural Networks: Wind Power Prediction using Knowledge Transfer from Region to Region and Between Different Task Domains—0
Gated Transfer Network for Transfer Learning—0
Semi-unsupervised Learning of Human Activity using Deep Generative ModelsCode0
Speaking style adaptation in Text-To-Speech synthesis using Sequence-to-sequence models with attention—0
Cross-Modal Distillation for RGB-Depth Person Re-Identification—0
Finding Answers from the Word of God: Domain Adaptation for Neural Networks in Biblical Question Answering—0
Accumulating Knowledge for Lifelong Online Learning—0
Transfer of Deep Reactive Policies for MDP PlanningCode0
K for the Price of 1: Parameter-efficient Multi-task and Transfer Learning—0
Improving Document Binarization via Adversarial Noise-Texture AugmentationCode0
Training neural audio classifiers with few dataCode0
Universal Language Model Fine-Tuning with Subword Tokenization for PolishCode0
Testing the Generalization Power of Neural Network Models Across NLI Benchmarks—0
How transferable are features in convolutional neural network acoustic models across languages?—0
Boosting pathology detection in infants by deep transfer learning from adult speech—0
Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data—0
Cross-domain aspect extraction for sentiment analysis: a transductive learning approach—0
Mechanisms for Integrated Feature Normalization and Remaining Useful Life Estimation Using LSTMs Applied to Hard-Disks—0
Machine Learning Methods for Track Classification in the AT-TPCCode0
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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