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 9976–10000 of 10307 papers

TitleStatusHype
Multilingual Training of Crosslingual Word Embeddings—0
Learning and Knowledge Transfer with Memory Networks for Machine Comprehension—0
(DE)^2 CO: Deep Depth Colorization—0
Deep 3D Face Identification—0
Discriminative Transfer Learning for General Image Restoration—0
Transfer learning for music classification and regression tasksCode0
Transductive Zero-Shot Learning with a Self-training dictionary approach—0
Overcoming Catastrophic Forgetting by Incremental Moment MatchingCode0
Smart Augmentation - Learning an Optimal Data Augmentation Strategy—0
Image-based Localization using Hourglass Networks—0
Knowledge Transfer for Melanoma Screening with Deep LearningCode0
Faster Reinforcement Learning Using Active SimulatorsCode0
Deep LSTM for Large Vocabulary Continuous Speech Recognition—0
I2T2I: Learning Text to Image Synthesis with Textual Data Augmentation—0
A Data Driven Approach for Compound Figure Separation Using Convolutional Neural Networks—0
Transfer Learning by Asymmetric Image Weighting for Segmentation across Scanners—0
Transfer Learning for Melanoma Detection: Participation in ISIC 2017 Skin Lesion Classification Challenge—0
Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning—0
On the Limits of Learning Representations with Label-Based Supervision—0
Functions that Emerge through End-to-End Reinforcement Learning - The Direction for Artificial General Intelligence -—0
Multi-Level and Multi-Scale Feature Aggregation Using Pre-trained Convolutional Neural Networks for Music Auto-taggingCode0
On the Behavior of Convolutional Nets for Feature Extraction—0
RGB-D Salient Object Detection Based on Discriminative Cross-modal Transfer Learning—0
Skin cancer reorganization and classification with deep neural network—0
Borrowing Treasures from the Wealthy: Deep Transfer Learning through Selective Joint Fine-tuningCode0
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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