Generalized zero-shot learning aims to recognize both seen and unseen classes with the help of semantic information that is shared among different classes. It inevitably requires consistent visual-semantic alignment. Existing approaches fine-tune the visual backbone by seen-class data to obtain semantic-related visual features, which may cause overfitting on seen classes with a limited number of training images. This paper proposes a novel visual and semantic prompt collaboration framework, which utilizes prompt tuning techniques for efficient feature adaptation. Specifically, we design a visual prompt to integrate the visual information for discriminative feature learning and a semantic prompt to integrate the semantic formation for visual-semantic alignment. To achieve effective prompt information integration, we further design a weak prompt fusion mechanism for the shallow layers and a strong prompt fusion mechanism for the deep layers in the network. Through the collaboration of visual and semantic prompts, we can obtain discriminative semantic-related features for generalized zero-shot image recognition. Extensive experiments demonstrate that our framework consistently achieves favorable performance in both conventional zero-shot learning and generalized zero-shot learning benchmarks compared to other state-of-the-art methods.

Generalized zero-shot learning (GZSL) requires that models are able to recognize classes they were trained on, and new classes they haven't seen before. Feature-generation approaches are popular due to their effectiveness in mitigating overfitting to the training classes. Existing generative approaches usually adopt simple discriminators for distribution or classification supervision, however, thus limiting their ability to generate visual features that are discriminative of and transferable to novel categories. To overcome this limitation and improve the quality of generated features, we propose a dual prototype contrastive augmented discriminator for the generative adversarial network. Specifically, we design a Dual Prototype Contrastive Network (DPCN), which leverages complementary information between visual space and semantic space through multi-task prototype contrastive learning. Contrastive learning of the visual prototypes enhances the ability of the generated features to distinguish between classes, while the contrastive learning of the semantic prototypes improves their transferability. Furthermore, we introduce margins into the contrastive learning process to ensure both intra-class compactness and inter-class separation. To demonstrate the effectiveness of the proposed approach, we conduct experiments on three widely-used zero-shot learning benchmark datasets, where DPCN achieves state-of-the-art performance for GZSL.

Deep learning has been increasingly adopted in many application areas. To construct valid deep learning models, developers must conform to certain computational constraints by carefully selecting appropriate neural architectures and hyperparameter values. For example, the kernel size hyperparameter of the 2D convolution operator cannot be overlarge to ensure that the height and width of the output tensor remain positive. Because model construction is largely manual and lacks necessary tooling support, it is possible to violate those constraints and raise type errors of deep learning models, causing either runtime exceptions or wrong output results. In this paper, we propose Refty, a refinement type-based tool for statically checking the validity of deep learning models ahead of job execution. Refty refines each type of deep learning operator with framework-independent logical formulae that describe the computational constraints on both tensors and hyperparameters. Given the neural architecture and hyperparameter domains of a model, Refty visits every operator, generates a set of constraints that the model should satisfy, and utilizes an SMT solver for solving the constraints. We have evaluated Refty on both individual operators and representative real-world models with various hyperparameter values under PyTorch and TensorFlow. We also compare it with an existing shape-checking tool. The experimental results show that Refty finds all the type errors and achieves 100% Precision and Recall, demonstrating its effectiveness.

Deep learning (DL) has been increasingly adopted by a variety of software-intensive systems. Developers mainly use GPUs to accelerate the training, testing, and deployment of DL models. However, the GPU memory consumed by a DL model is often unknown to them before the DL job executes. Therefore, an improper choice of neural architecture or hyperparameters can cause such a job to run out of the limited GPU memory and fail. Our recent empirical study has found that many DL job failures are due to the exhaustion of GPU memory. This leads to a horrendous waste of computing resources and a significant reduction in development productivity. In this paper, we propose DNNMem, an accurate estimation tool for GPU memory consumption of DL models. DNNMem employs an analytic estimation approach to systematically calculate the memory consumption of both the computation graph and the DL framework runtime. We have evaluated DNNMem on 5 real-world representative models with different hyperparameters under 3 mainstream frameworks (TensorFlow, PyTorch, and MXNet). Our extensive experiments show that DNNMem is effective in estimating GPU memory consumption.