Second, on channel aspect, representation exhibits diversity ondifferent channels. But the scarce data can not enable ViTs to learn strong enough representation for accurate recognition.

As the state-of-the-art framework, i.e., RecEraser, naturally achieves full unlearning completeness,

We propose an adaptive discriminator augmentation mechanism that significantly stabilizes training in limited data regimes.

Controllable generation is one of the key requirements for successful adoptionof deep generative models in real-world applications, but it still remains as a greatchallenge.

Neural network quantization is one of the most effective ways to improve the efficiency of neural networks. Quantization allows weights and activations to be represented in low bit-width formats, e.g. 8 bit integers (INT8).

However, it is unclear how to best fit low-rank RNNs to data consisting of noisy observations of an underlying stochastic system. Here, we propose to fit stochastic low-rank RNNs with variational sequential Monte Carlo methods.