A technical challenge of deep learning is recognizing target classes without seen data. Zero-shot learning leverages semantic representations such as attributes or class prototypes to bridge source and target classes. Existing standard zero-shot learning methods may be prone to overfitting the seen data of source classes as they are blind to the semantic representations of target classes. In this paper, we study generalized zero-shot learning that assumes accessible to target classes for unseen data during training, and prediction on unseen data is made by searching on both source and target classes. We propose a novel Deep Calibration Network (DCN) approach towards this generalized zero-shot learning paradigm, which enables simultaneous calibration of deep networks on the confidence of source classes and uncertainty of target classes. Our approach maps visual features of images and semantic representations of class prototypes to a common embedding space such that the compatibility of seen data to both source and target classes are maximized. We show superior accuracy of our approach over the state of the art on benchmark datasets for generalized zero-shot learning, including AwA, CUB, SUN, and aPY.

We argue that this is due, in part, to a common feature of many interpretability methods: they analyze model behavior by using a particular dataset.

Large-scale generative models have shown impressive image-generation capabilities, propelled by massive data.

To bridge this gap, we model the strategic interactions between the defender and dynamic attackers as a minimax game. Based on the analysis of the game, we design an interactive defense mechanism FedGame. We prove that under mild assumptions, the global model trained with FedGame under backdoor attacks is close to that trained without attacks.

The vulnerability of deep image classification networks to adversarial attack is nowwellknown,butlesswellunderstood.

In recent years, machine learning models have been shown to be vulnerable to backdoor attacks.

Almost Linearity T argeting, based on medium-scale almost linearity assumptions.

The OOD results quoted in the papers use a range of classification network architectures.