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 8701–8725 of 10307 papers

TitleStatusHype
A Deep Learning Study on Osteosarcoma Detection from Histological Images—0
A Deep Learning Sequential Decoder for Transient High-Density Electromyography in Hand Gesture Recognition Using Subject-Embedded Transfer Learning—0
Policy-Conditioned Uncertainty Sets for Robust Markov Decision Processes—0
SuperLoRA: Parameter-Efficient Unified Adaptation of Multi-Layer Attention Modules—0
Super-Resolution and Image Re-projection for Iris Recognition—0
Polish Medical Exams: A new dataset for cross-lingual medical knowledge transfer assessment—0
Polite Task-oriented Dialog Agents: To Generate or to Rewrite?—0
A Deep Learning Perspective on the Origin of Facial Expressions—0
Polymer Informatics with Multi-Task Learning—0
A Deep Learning Method for Complex Human Activity Recognition Using Virtual Wearable Sensors—0
The Impact of Selectional Preference Agreement on Semantic Relational Similarity—0
POMDP-based dialogue manager adaptation to extended domains—0
POMP: Probability-driven Meta-graph Prompter for LLMs in Low-resource Unsupervised Neural Machine Translation—0
A Deep Learning Framework for Lifelong Machine Learning—0
A Deep Learning based Wearable Healthcare IoT Device for AI-enabled Hearing Assistance Automation—0
SuperTML: Two-Dimensional Word Embedding and Transfer Learning Using ImageNet Pretrained CNN Models for the Classifications on Tabular Data—0
Supervised and Contrastive Self-Supervised In-Domain Representation Learning for Dense Prediction Problems in Remote Sensing—0
Pose-Guided Knowledge Transfer for Object Part Segmentation—0
Supervised and Unsupervised Transfer Learning for Question Answering—0
A Deep Learning-Based GPR Forward Solver for Predicting B-Scans of Subsurface Objects—0
Positional Attention-based Frame Identification with BERT: A Deep Learning Approach to Target Disambiguation and Semantic Frame Selection—0
A Deep Learning-based Compression and Classification Technique for Whole Slide Histopathology Images—0
The Importance of the Instantaneous Phase for classification using Convolutional Neural Networks—0
Post-Earthquake Assessment of Buildings Using Deep Learning—0
POSTECH Submission on Duolingo Shared Task—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