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Found 10 papers, 7 papers with code
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.
On Evaluating Adversarial Robustness of Large Vision-Language Models
Large vision-language models (VLMs) such as GPT-4 have achieved unprecedented performance in response generation, especially with visual inputs, enabling more creative and adaptable interaction than large language models such as ChatGPT.
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.
A Recipe for Watermarking Diffusion Models
Diffusion models (DMs) have demonstrated advantageous potential on generative tasks.
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
FS-BAN: Born-Again Networks for Domain Generalization Few-Shot Classification
This improved generalization motivates us to study BAN for DG-FSC, and we show that BAN is promising to address the domain shift encountered in DG-FSC.
Revisiting Label Smoothing and Knowledge Distillation Compatibility: What was Missing?
Critically, there is no effort to understand and resolve these contradictory findings, leaving the primal question -- to smooth or not to smooth a teacher network?
A Closer Look at Few-shot Image Generation
Informed by our analysis and to slow down the diversity degradation of the target generator during adaptation, our second contribution proposes to apply mutual information (MI) maximization to retain the source domain's rich multi-level diversity information in the target domain generator.
Ranked #2 on
10-shot image generation
on Babies
To Smooth or not to Smooth? On Compatibility between Label Smoothing and Knowledge Distillation
On the contrary, Shen et al. [2] claim that LS enlarges the distance between semantically similar classes; therefore a LS-trained teacher is compatible with KD.
Explanation-Guided Training for Cross-Domain Few-Shot Classification
It leverages on the explanation scores, obtained from existing explanation methods when applied to the predictions of FSC models, computed for intermediate feature maps of the models.
Ranked #8 on
Cross-Domain Few-Shot
on ISIC2018
