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Found 11 papers, 6 papers with code
A Survey on Generative Modeling with Limited Data, Few Shots, and Zero Shot
In machine learning, generative modeling aims to learn to generate new data statistically similar to the training data distribution.
AdAM: Few-Shot Image Generation via Adaptation-Aware Kernel Modulation
However, a major limitation of existing methods is that their knowledge preserving criteria consider only source domain/task and fail to consider target domain/adaptation in selecting source knowledge, casting doubt on their suitability for setups of different proximity between source and target domain.
Exploring Incompatible Knowledge Transfer in Few-shot Image Generation
To this end, we propose knowledge truncation to mitigate this issue in FSIG, which is a complementary operation to knowledge preservation and is implemented by a lightweight pruning-based method.
Re-thinking Model Inversion Attacks Against Deep Neural Networks
Recently, several algorithms for MI have been proposed to improve the attack performance.
Fair Generative Models via Transfer Learning
We find that our fairTL can learn expressive sample generation during pre-training, thanks to the large (biased) dataset.
Few-shot Image Generation via Adaptation-Aware Kernel Modulation
However, a major limitation of existing methods is that their knowledge preserving criteria consider only source domain/source task, and they fail to consider target domain/adaptation task in selecting source model's knowledge, casting doubt on their suitability for setups of different proximity between source and target domain.
Ranked #1 on
10-shot image generation
on Babies
Revisit Multimodal Meta-Learning through the Lens of Multi-Task Learning
Second, inspired by hard parameter sharing in multi-task learning and a new interpretation of related work, we propose a new multimodal meta-learner that outperforms existing work by considerable margins.
Multi-focus Image Fusion for Visual Sensor Networks
Image fusion in visual sensor networks (VSNs) aims to combine information from multiple images of the same scene in order to transform a single image with more information.
Deep Artifact-Free Residual Network for Single Image Super-Resolution
In this way, we are able to use the ground-truth images as target and avoid misleading the network due to artifacts in difference image.
Fine-grained wound tissue analysis using deep neural network
In particular, seemingly, all previous approaches have assumed only 3 tissue types in the chronic wounds, while these wounds commonly exhibit 7 distinct tissue types that presence of each one changes the treatment procedure.
Aircraft Fuselage Defect Detection using Deep Neural Networks
To ensure flight safety of aircraft structures, it is necessary to have regular maintenance using visual and nondestructive inspection (NDI) methods.
