Search Results for author:
Found 7 papers, 5 papers with code
Anti-Compression Contrastive Facial Forgery Detection
To enhance the performance for such models, we consider the weak compressed and strong compressed data as two views of the original data and they should have similar representation and relationships with other samples.
ContraFeat: Contrasting Deep Features for Semantic Discovery
In this paper, we propose a model that automates this process and achieves state-of-the-art semantic discovery performance.
Commutative Lie Group VAE for Disentanglement Learning
Instead, we propose to encode the data variations with groups, a structure not only can equivariantly represent variations, but can also be adaptively optimized to preserve the properties of data variations.
Where and What? Examining Interpretable Disentangled Representations
We thus impose a perturbation on a certain dimension of the latent code, and expect to identify the perturbation along this dimension from the generated images so that the encoding of simple variations can be enforced.
Approximated Bilinear Modules for Temporal Modeling
Specifically, we show how two-layer subnets in CNNs can be converted to temporal bilinear modules by adding an auxiliary-branch.
Learning Disentangled Representations with Latent Variation Predictability
Given image pairs generated by latent codes varying in a single dimension, this varied dimension could be closely correlated with these image pairs if the representation is well disentangled.
B-CNN: Branch Convolutional Neural Network for Hierarchical Classification
In this way we show that CNN based models can be forced to learn successively coarse to fine concepts in the internal layers at the output stage, and that hierarchical prior knowledge can be adopted to boost CNN models' classification performance.
