Deep Learning
CODA: Generalizing to Open and Unseen Domains with Compaction and Disambiguation
To meaningfully regularize the model's decision boundary, CODA introduces virtual unknown classes
A Long N-step Surrogate Stage Reward for Deep Reinforcement Learning Junmin Zhong Arizona State University Ruofan Wu Arizona State University Jennie Si Arizona State University
DDPG (TD3) [7], have demonstrated their great potential. Contributions . 1) We introduce a new, simple yet effective surrogate reward They usually proceed as follows.
Face Reconstruction from Facial Templates by Learning Latent Space of a Generator Network
Among potential attacks against FR systems [Galbally et al., 2014, Biggio et al., 2015, Hadid et al., 2015, Mai et al., 2018, Marcel et al., 2023], the template inversion (TI) attack significantly jeopardizes the users' privacy. In a TI attack, the adversary gains access to templates stored in the FR system's database and aims
Composing Parameter-Efficient Modules with Arithmetic Operations
Our approach requires no additional training and enables highly flexible module composition.
Tanh Works Better With Asymmetry
Batch Normalization is commonly located in front of activation functions, as proposed by the original paper.
Disentangled Counterfactual Learning for Physical Audiovisual Commonsense Reasoning Supplementary Material Anonymous Author(s) Affiliation Address email
Moreover, we show more visualization results in experiments. To ensure a fair comparison, we used the fusion and optimization method as same as Latefusion. When k=1, it means that the object's physical properties are only related to itself, while As described in Section 3.1 in our paper, we represent audio Table 2: Performance comparison between our proposed DSE-audio and existing baseline methods. As shown in Table 2, we compare our method with other baseline methods. In Figure 6, we show a few additional examples of clustering using dynamic factors.
