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 8351–8375 of 10307 papers

TitleStatusHype
Don't Use English Dev: On the Zero-Shot Cross-Lingual Evaluation of Contextual Embeddings—0
Abu-MaTran: Automatic building of Machine Translation—0
On the Generalization Gap in Reparameterizable Reinforcement Learning—0
On the Generalization of Handwritten Text Recognition Models—0
Stratified Transfer Learning for Cross-domain Activity Recognition—0
Bayesian Active Learning in the Presence of Nuisance Parameters—0
On the Hidden Negative Transfer in Sequential Transfer Learning for Domain Adaptation from News to Tweets—0
On the Hyperparameter Loss Landscapes of Machine Learning Models: An Exploratory Study—0
On the impact of incorporating task-information in learning-based image denoising—0
On the impact of measure pre-conditionings on general parametric ML models and transfer learning via domain adaptation—0
Deep Feature Learning from a Hospital-Scale Chest X-ray Dataset with Application to TB Detection on a Small-Scale Dataset—0
On the Intrinsic Limits to Representationally-Adaptive Machine-Learning—0
Streamlining Brain Tumor Classification with Custom Transfer Learning in MRI Images—0
On the Limits of Learning Representations with Label-Based Supervision—0
On the Limits to Multi-Modal Popularity Prediction on Instagram -- A New Robust, Efficient and Explainable Baseline—0
On the low-shot transferability of [V]-Mamba—0
On the Mechanisms of Adversarial Data Augmentation for Robust and Adaptive Transfer Learning—0
A first step towards automated species recognition from camera trap images of mammals using AI in a European temperate forest—0
On the Pitfalls of Learning with Limited Data: A Facial Expression Recognition Case Study—0
The Geometry of Self-supervised Learning Models and its Impact on Transfer Learning—0
On The Relationship between Visual Anomaly-free and Anomalous Representations—0
Unveiling the Tapestry: the Interplay of Generalization and Forgetting in Continual Learning—0
On the Robustness of Arabic Speech Dialect Identification—0
On the Role of Neural Collapse in Transfer Learning—0
On the Role of Parallel Data in Cross-lingual Transfer Learning—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