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 9376–9400 of 10307 papers

TitleStatusHype
Scenes-Objects-Actions: A Multi-Task, Multi-Label Video Dataset—0
Deep Multi-Task Learning to Recognise Subtle Facial Expressions of Mental States—0
Learning Single-View 3D Reconstruction with Limited Pose SupervisionCode0
Gromov-Wasserstein Alignment of Word Embedding Spaces—0
AISHELL-2: Transforming Mandarin ASR Research Into Industrial Scale—0
Automatic Lung Cancer Prediction from Chest X-ray Images Using Deep Learning Approach—0
DADA: Deep Adversarial Data Augmentation for Extremely Low Data Regime ClassificationCode0
The Impact of Preprocessing on Deep Representations for Iris Recognition on Unconstrained Environments—0
Adapting Word Embeddings to New Languages with Morphological and Phonological Subword RepresentationsCode0
Efficient keyword spotting using time delay neural networks—0
Migrating Knowledge between Physical Scenarios based on Artificial Neural Networks—0
Sentence Embeddings in NLI with Iterative Refinement EncodersCode0
Amobee at IEST 2018: Transfer Learning from Language Models—0
Zero-shot Transfer Learning for Semantic Parsing—0
Meta-Learning for Low-Resource Neural Machine Translation—0
Financial Aspect-Based Sentiment Analysis using Deep Representations—0
Transfer Learning for Estimating Causal Effects using Neural Networks—0
Video Jigsaw: Unsupervised Learning of Spatiotemporal Context for Video Action Recognition—0
Multidomain Document Layout Understanding using Few Shot Object Detection—0
Lessons from Natural Language Inference in the Clinical DomainCode0
Neural Relation Extraction via Inner-Sentence Noise Reduction and Transfer Learning—0
DeeSIL: Deep-Shallow Incremental Learning—0
XL-NBT: A Cross-lingual Neural Belief Tracking FrameworkCode0
CellLineNet: End-to-End Learning and Transfer Learning For Multiclass Epithelial Breast cell Line Classification via a Convolutional Neural Network—0
Collaborative Pressure Ulcer Prevention: An Automated Skin Damage and Pressure Ulcer Assessment Tool for Nursing Professionals, Patients, Family Members and Carers—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