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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4676–4700 of 10307 papers

TitleStatusHype
ACE: Zero-Shot Image to Image Translation via Pretrained Auto-Contrastive-EncoderCode0
Modular Deep Learning—0
Transfer Learning Enhanced Full Waveform Inversion—0
SU-Net: Pose estimation network for non-cooperative spacecraft on-orbitCode0
A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT—0
On Inductive Biases for Machine Learning in Data Constrained SettingsCode0
Exploring the Limits of Transfer Learning with Unified Model in the Cybersecurity Domain—0
Multiobjective Evolutionary Pruning of Deep Neural Networks with Transfer Learning for improving their Performance and Robustness—0
Boosting Convolutional Neural Networks' Protein Binding Site Prediction Capacity Using SE(3)-invariant transformers, Transfer Learning and Homology-based Augmentation—0
Multilingual Content Moderation: A Case Study on RedditCode0
HomoDistil: Homotopic Task-Agnostic Distillation of Pre-trained Transformers—0
A Comprehensive Evaluation Study on Risk Level Classification of Melanoma by Computer Vision on ISIC 2016-2020 Datasets—0
Few-shot Multimodal Multitask Multilingual Learning—0
Why Is Public Pretraining Necessary for Private Model Training?—0
Adversarial Contrastive Distillation with Adaptive Denoising—0
Self-Supervised Representation Learning from Temporal Ordering of Automated Driving Sequences—0
Cross-Domain Label Propagation for Domain Adaptation with Discriminative Graph Self-Learning—0
sMRI-PatchNet: A novel explainable patch-based deep learning network for Alzheimer's disease diagnosis and discriminative atrophy localisation with Structural MRI—0
Deep Implicit Distribution Alignment Networks for Cross-Corpus Speech Emotion Recognition—0
Generative Causal Representation Learning for Out-of-Distribution Motion ForecastingCode0
Learning to diagnose cirrhosis from radiological and histological labels with joint self and weakly-supervised pretraining strategies—0
A Survey of Geometric Optimization for Deep Learning: From Euclidean Space to Riemannian Manifold—0
Revisiting Hidden Representations in Transfer Learning for Medical ImagingCode0
Distributed Learning in Heterogeneous Environment: federated learning with adaptive aggregation and computation reduction—0
FOSI: Hybrid First and Second Order OptimizationCode0
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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