Deep neural models, in recent years, have been successful in almost every field, even
solving the most complex problem statements. However, these models are huge in size with …

The remarkable performance of deep Convolutional neural networks (CNNs) is generally
attributed to their deeper and wider architectures, which can come with significant …

In this paper, we explore a novel and ambitious knowledge-transfer task, termed Knowledge
Factorization (KF). The core idea of KF lies in the modularization and assemblability of …

In recent years, deep neural networks have been successful in both industry and academia,
especially for computer vision tasks. The great success of deep learning is mainly due to its …

Vision-language models trained with contrastive learning on large-scale noisy data are
becoming increasingly popular for zero-shot recognition problems. In this paper we improve …

Channel (or 3D filter) pruning serves as an effective way to accelerate the inference of
neural networks. There has been a flurry of algorithms that try to solve this practical problem …

Abstract Conditional Generative Adversarial Networks (cGANs) have enabled controllable
image synthesis for many computer vision and graphics applications. However, recent …

Most of the few-shot learning methods learn to transfer knowledge from datasets with
abundant labeled data (ie, the base set). From the perspective of class space on base set …

Performing knowledge transfer from a large teacher network to a smaller student is a
popular task in modern deep learning applications. However, due to growing dataset sizes …

We study the problem of model extraction in natural language processing, in which an
adversary with only query access to a victim model attempts to reconstruct a local copy of …