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 4026–4050 of 10307 papers

TitleStatusHype
Few-Shot Causal Representation Learning for Out-of-Distribution Generalization on Heterogeneous Graphs—0
Hate Speech Detection and Racial Bias Mitigation in Social Media based on BERT model—0
Few-shot Classification via Ensemble Learning with Multi-Order Statistics—0
FEW SHOT CROP MAPPING USING TRANSFORMERS AND TRANSFER LEARNING WITH SENTINEL-2 TIME SERIES: CASE OF KAIROUAN TUNISIA—0
Few-Shot Cross-Lingual TTS Using Transferable Phoneme Embedding—0
Decoding Working-Memory Load During n-Back Task Performance from High Channel NIRS Data—0
Few-Shot Dialogue Summarization via Skeleton-Assisted Prompt Transfer in Prompt Tuning—0
Few-Shot Domain Adaptation for Grammatical Error Correction via Meta-Learning—0
Few-shot fault diagnosis based on multi-scale graph convolution filtering for industry—0
Automated Scoring of Clinical Expressive Language Evaluation Tasks—0
A Comparative Study on Transfer Learning and Distance Metrics in Semantic Clustering over the COVID-19 Tweets—0
Decision Support System for Detection and Classification of Skin Cancer using CNN—0
Covariate-Elaborated Robust Partial Information Transfer with Conditional Spike-and-Slab Prior—0
Automated Pruning for Deep Neural Network Compression—0
A multilingual training strategy for low resource Text to Speech—0
Few-Shot Learning-Based Human Activity Recognition—0
A Multi-stage Transfer Learning Framework for Diabetic Retinopathy Grading on Small Data—0
Few-Shot Learning for Annotation-Efficient Nucleus Instance Segmentation—0
Concurrent Discrimination and Alignment for Self-Supervised Feature Learning—0
Condensed Sample-Guided Model Inversion for Knowledge Distillation—0
When Few-Shot Learning Meets Video Object Detection—0
Gaze-Net: Appearance-Based Gaze Estimation using Capsule Networks—0
Few-Shot Load Forecasting Under Data Scarcity in Smart Grids: A Meta-Learning Approach—0
Few-Shot Meta-Denoising—0
Deciphering and Optimizing Multi-Task Learning: a Random Matrix Approach—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