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 5351–5400 of 10307 papers

TitleStatusHype
A Comparative Analysis of Machine Learning Approaches for Automated Face Mask Detection During COVID-19Code0
Fine-Tuning Large Neural Language Models for Biomedical Natural Language Processing—0
Chimpanzee voice prints? Insights from transfer learning experiments from human voices—0
Confidence-Aware Subject-to-Subject Transfer Learning for Brain-Computer Interface—0
MissMarple : A Novel Socio-inspired Feature-transfer Learning Deep Network for Image Splicing Detection—0
Know Thy Strengths: Comprehensive Dialogue State Tracking DiagnosticsCode0
One System to Rule them All: a Universal Intent Recognition System for Customer Service Chatbots—0
Cross-Domain Generalization and Knowledge Transfer in Transformers Trained on Legal Data—0
Maximum Bayes Smatch Ensemble Distillation for AMR ParsingCode0
Exploring Neural Models for Query-Focused SummarizationCode1
On the Use of External Data for Spoken Named Entity RecognitionCode0
Epigenomic language models powered by Cerebras—0
CLIP-Lite: Information Efficient Visual Representation Learning with Language SupervisionCode1
Large Language Models are not Models of Natural Language: they are Corpus Models—0
Long-tail Recognition via Compositional Knowledge Transfer—0
VL-Adapter: Parameter-Efficient Transfer Learning for Vision-and-Language TasksCode1
Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations in a Label-Abundant SetupCode0
Automated Customization of On-Thing Inference for Quality-of-Experience Enhancement—0
Technical Language Supervision for Intelligent Fault Diagnosis in Process Industry—0
Analysis and Prediction of NLP Models Via Task EmbeddingsCode0
Automated tabulation of clinical trial results: A joint entity and relation extraction approach with transformer-based language representationsCode0
PMFL: Partial Meta-Federated Learning for heterogeneous tasks and its applications on real-world medical recordsCode0
KartalOl: Transfer learning using deep neural network for iris segmentation and localization: New dataset for iris segmentationCode0
Transfer learning using deep neural networks for Ear Presentation Attack Detection: New Database for PADCode0
Best Arm Identification under Additive Transfer Bandits—0
Pareto Domain AdaptationCode0
Multinational Address Parsing: A Zero-Shot EvaluationCode1
ADD: Frequency Attention and Multi-View based Knowledge Distillation to Detect Low-Quality Compressed Deepfake ImagesCode0
Handwritten Mathematical Expression Recognition via Attention Aggregation based Bi-directional Mutual LearningCode1
Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Tasks—0
Prototypical Model with Novel Information-theoretic Loss Function for Generalized Zero Shot Learning—0
CSG0: Continual Urban Scene Generation with Zero Forgetting—0
Flexible Option LearningCode0
Transfer learning to improve streamflow forecasts in data sparse regions—0
CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks—0
Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning—0
SemanticStyleGAN: Learning Compositional Generative Priors for Controllable Image Synthesis and EditingCode1
Transferring Unconditional to Conditional GANs with Hyper-ModulationCode1
TransBoost: A Boosting-Tree Kernel Transfer Learning Algorithm for Improving Financial InclusionCode1
Overcome Anterograde Forgetting with Cycled Memory Networks—0
PointCLIP: Point Cloud Understanding by CLIPCode1
Divergent representations of ethological visual inputs emerge from supervised, unsupervised, and reinforcement learning—0
Learning Curves for Continual Learning in Neural Networks: Self-Knowledge Transfer and Forgetting—0
A Survey: Deep Learning for Hyperspectral Image Classification with Few Labeled SamplesCode1
Fast Data-Driven Adaptation of Radar Detection via Meta-Learning—0
Self-Supervised Material and Texture Representation Learning for Remote Sensing TasksCode1
Transfer Learning in Conversational Analysis through Reusing Preprocessing Data as Supervisors—0
Contrastive Cross-domain Recommendation in MatchingCode1
Active Learning for Domain Adaptation: An Energy-Based ApproachCode1
OW-DETR: Open-world Detection TransformerCode1
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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