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 6526–6550 of 10307 papers

TitleStatusHype
Deep Learning Classification of Lake ZooplanktonCode0
ConvNets vs. Transformers: Whose Visual Representations are More Transferable?—0
An empirical investigation into audio pipeline approaches for classifying bird species—0
Joint Pilot Design and Channel Estimation using Deep Residual Learning for Multi-Cell Massive MIMO under Hardware Impairments—0
Hand Pose Classification Based on Neural Networks—0
How Self-Supervised Learning Can be Used for Fine-Grained Head Pose Estimation?—0
Automated Audio Captioning using Transfer Learning and Reconstruction Latent Space Similarity Regularization—0
Towards artificially intelligent recycling Improving image processing for waste classification—0
Transfer Learning Gaussian Anomaly Detection by Fine-tuning Representations—0
Machine Translation of Low-Resource Indo-European Languages—0
Deep Transfer Learning for Identifications of Slope Surface Cracks—0
Improving Similar Language Translation With Transfer Learning—0
Offensive Language and Hate Speech Detection with Deep Learning and Transfer Learning—0
Feature-Supervised Action Modality Transfer—0
Ensemble Augmentation for Deep Neural Networks Using 1-D Time Series Vibration DataCode0
Basis Scaling and Double Pruning for Efficient Inference in Network-Based Transfer Learning—0
Incremental Feature Learning For Infinite Data—0
Detecting Requirements Smells With Deep Learning: Experiences, Challenges and Future Work—0
A Data Augmented Approach to Transfer Learning for Covid-19 Detection—0
Ada-VSR: Adaptive Video Super-Resolution with Meta-Learning—0
Improved Speech Emotion Recognition using Transfer Learning and Spectrogram Augmentation—0
Robust Transfer Learning with Pretrained Language Models through Adapters—0
WeChat Neural Machine Translation Systems for WMT21—0
Terabyte-scale supervised 3D training and benchmarking dataset of the mouse kidney—0
Semi-weakly Supervised Contrastive Representation Learning for Retinal Fundus ImagesCode0
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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