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Found 9 papers, 5 papers with code
Developmental Pretraining (DPT) for Image Classification Networks
In the backdrop of increasing data requirements of Deep Neural Networks for object recognition that is growing more untenable by the day, we present Developmental PreTraining (DPT) as a possible solution.
Sem-CS: Semantic CLIPStyler for Text-Based Image Style Transfer
However, the ground semantics of objects in the style transfer output is lost due to style spill-over on salient and background objects (content mismatch) or over-stylization.
Synthpop++: A Hybrid Framework for Generating A Country-scale Synthetic Population
Our experimental results show that synthetic population can realistically simulate the population for various administrative units of India, producing real-scale, detailed data at the desired level of zoom -- from cities, to districts, to states, eventually combining to form a country-scale synthetic population.
SEM-CS: Semantic CLIPStyler for Text-Based Image Style Transfer
Sem-CS first segments the content image into salient and non-salient objects and then transfers artistic style based on a given style text description.
ContextCLIP: Contextual Alignment of Image-Text pairs on CLIP visual representations
However, it showed negative transfer performance on standard datasets, e. g., BirdsNAP, RESISC45, and MNIST.
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine Learning
This makes it prone to gradient tampering and privacy leakage by a malicious aggregator.
SCOTCH: An Efficient Secure Computation Framework for Secure Aggregation
Federated learning enables multiple data owners to jointly train a machine learning model without revealing their private datasets.
S++: A Fast and Deployable Secure-Computation Framework for Privacy-Preserving Neural Network Training
In recent times, ReLU has been found to converge much faster and be more computationally efficient as compared to non-linear functions like sigmoid or tanh.
Differential Euler: Designing a Neural Network approximator to solve the Chaotic Three Body Problem
In this paper, we propose a detailed experimental setup to determine the feasibility of using neural networks to solve the three body problem up to a certain number of time steps.
