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 9226–9250 of 10307 papers

TitleStatusHype
Controllability, Multiplexing, and Transfer Learning in Networks using Evolutionary LearningCode0
Model-guided Multi-path Knowledge Aggregation for Aerial Saliency Prediction—0
Interactive dimensionality reduction using similarity projections—0
Unsupervised Transfer Learning for Spoken Language Understanding in Intelligent AgentsCode0
A Framework of Transfer Learning in Object Detection for Embedded SystemsCode0
Improving speech emotion recognition via Transformer-based Predictive Coding through transfer learning—0
A Fully Automated System for Sizing Nasal PAP Masks Using Facial Photographs—0
M2M-GAN: Many-to-Many Generative Adversarial Transfer Learning for Person Re-Identification—0
Doc2Im: document to image conversion through self-attentive embedding—0
Knowledge Transfer via Distillation of Activation Boundaries Formed by Hidden NeuronsCode0
Analysis of Multilingual Sequence-to-Sequence speech recognition systems—0
Baselines for Reinforcement Learning in Text Games—0
Transfer Learning from LDA to BiLSTM-CNN for Offensive Language Detection in Twitter—0
Transfer learning of language-independent end-to-end ASR with language model fusion—0
Language model integration based on memory control for sequence to sequence speech recognition—0
Micro-Attention for Micro-Expression recognitionCode0
Revealing Fine Structures of the Retinal Receptive Field by Deep Learning Networks—0
Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data TasksCode0
Learning to Rank Query Graphs for Complex Question Answering over Knowledge GraphsCode0
Zero-Shot Transfer VQA Dataset—0
Universal Sentence Encoder for English—0
Multilingual NMT with a language-independent attention bridgeCode0
Addressing word-order Divergence in Multilingual Neural Machine Translation for extremely Low Resource Languages—0
An Analysis of Encoder Representations in Transformer-Based Machine Translation—0
Cogni-Net: Cognitive Feature Learning through Deep Visual PerceptionCode0
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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