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Found 7 papers, 3 papers with code
Dual Adversarial Alignment for Realistic Support-Query Shift Few-shot Learning
The key feature of RSQS is that the individual samples in a meta-task are subjected to multiple distribution shifts in each meta-task.
Overcoming Forgetting Catastrophe in Quantization-Aware Training
Afterward, we exploit replay data (a subset of old task data) for retraining in new tasks to alleviate the forgetting problem.
PGADA: Perturbation-Guided Adversarial Alignment for Few-shot Learning Under the Support-Query Shift
Few-shot learning methods aim to embed the data to a low-dimensional embedding space and then classify the unseen query data to the seen support set.
AlignQ: Alignment Quantization With ADMM-Based Correlation Preservation
Afterward, our theoretical analysis indicates that the significant changes in data correlations after the quantization induce a large quantization error.
Attack as the Best Defense: Nullifying Image-to-image Translation GANs via Limit-aware Adversarial Attack
To improve efficiency, we introduce the limit-aware random gradient-free estimation and the gradient sliding mechanism to estimate the gradient that adheres to the adversarial limit, i. e., the pixel value limitations of the adversarial example.
Video Event Detection by Inferring Temporal Instance Labels
To solve this problem, we propose a large-margin formulation which treats the instance labels as hidden latent variables, and simultaneously infers the instance labels as well as the instance-level classification model.
Sample-Specific Late Fusion for Visual Category Recognition
However, the existing methods generally use a fixed fusion weight for all the scores of a classifier, and thus fail to optimally determine the fusion weight for the individual samples.
