Abstract: A multi-task identification method, a training method, an electronic device, and a storage medium are provided, which relate to a field of an artificial intelligence technology, in particular to fields of deep learning, image processing and computer vision technologies, and may be applied to scenarios such as human faces. A specific implementation solution includes: obtaining first intermediate feature data according to an image to be identified; selecting a feature extraction strategy having a greatest matching degree with the image to be identified from a plurality of feature extraction strategies based on a target selection strategy and the first intermediate feature data, so as to obtain a target feature extraction strategy; processing the first intermediate feature data based on the target feature extraction strategy, to obtain second intermediate feature data; and obtaining a multi-task identification result for the image to be identified according to the second intermediate feature data.

Abstract: A method for face recognition is disclosed. The method includes: obtaining an image to be recognized; extracting an image feature of the image to be recognized; obtaining a fused feature corresponding to a reference image of reference image; determining a similarity between the image feature of the image to be recognized and the fused feature corresponding to the reference image of the reference images to obtain a determination result of the similarity; and determining, based on the obtained determination result of the similarity, a face recognition result of the image to be recognized. It can be learned that recognition precision of a human face having a shielding object and a human face not having the shielding object may be ensured by means of this solution.

Abstract: A method of generating a pre-training model, an electronic device, and a storage medium, which relate to a field of an artificial intelligence technology, in particular to a field of a computer vision and deep learning technology. The method includes: determining, for each of a plurality of tasks, a performance index set corresponding to a candidate model structure set, the candidate model structure set is determined from a plurality of model structures included in a search space, and the search space is a super-network-based search space; determining, from the candidate model structure set, a target model structure according to a plurality of performance index sets, the target model structure is a model structure meeting a performance index condition, and the plurality of performance index sets correspond to the plurality of tasks respectively; and determining the target model structure as the pre-training model.

Abstract: A method of generating a pre-training model, an electronic device and a storage medium, which relate to a field of an artificial intelligence technology, in particular to a computer vision and deep learning technology. The method includes: determining a performance index set corresponding to a candidate model structure set, the candidate model structure set is determined from a plurality of model structures included in a search space, and the search space is a super-network-based search space; determining, from the candidate model structure set, a target model structure corresponding to each chip according to the performance index set, each target model structure is a model structure meeting a performance index condition; and determining, for each chip, the target model structure corresponding to the chip as a pre-training model corresponding to the chip, the chip is configured to run the pre-training model corresponding to the chip.

Abstract: The disclosure provides a method for determining a pre-training model, an electronic device and a storage medium, relates to a technical field of computer vision and deep learning, and can be applied to scenes such as image processing and image recognition. The method includes: obtaining a plurality of candidate models; obtaining a structural code of each candidate model by performing structural coding according to model structures of the plurality of candidate models; obtaining a frequency domain code of each candidate model by mapping the structural code of each candidate model using a trained encoder; predicting a model performance parameter of each candidate model according to the frequency domain code of each candidate model; and determining a target model from the plurality of candidate models as a pre-training model according to the model performance parameter of each candidate model.

Abstract: A method for generating a pre-trained model, includes: extracting, by each of candidate models that are selected from a model set, features from samples in a test set, to obtain features output by each of the candidate models; obtaining fusion features by fusing features output by the candidate models; obtaining prediction information by performing a preset target recognition task based on the fusion features; determining combination performance of the candidate models based on difference between the prediction information and standard information of the samples; and generating the pre-trained model based on the candidate models in response to the combination performance satisfying a preset performance index.

Abstract: Network architecture search (NAS) received a lot of attention. The supernet-based differentiable approach is popular because it can effectively share the weights and lead to more efficient search. However, the mismatch between the architecture and weights caused by weight sharing still exists. Moreover, the coupling effects among different operators are also neglected. To alleviate these problems, embodiments of an effective NAS methodology by similarity-based operator ranking are presented herein. With the aim of approximating each layer's output in the supernet, a similarity-based operator ranking based on statistical random comparison is used. In one or more embodiments, then the operator that possibly causes the least change to feature distribution discrepancy is pruned. In one or more embodiments, a fair sampling process may be used to mitigate the operators' Matthew effect that happened frequently in previous supernet approaches.

Abstract: Embodiments of the present disclosure provide a method and apparatus for determining a network model pruning strategy, a device and a storage medium. The method may include: generating a BN threshold search space using configuration information of the BN threshold search space for a network model for a target hardware; generating a pruning strategy code generator using the BN threshold search space; randomly generating a BN threshold code using the pruning strategy code generator; decoding the BN threshold code to obtain a candidate BN threshold; and determining a target pruning strategy of the network model for the target hardware, based on a pruning accuracy loss of the network model corresponding to the candidate BN threshold.