Unsupervised domain adaptation in person re-identification resorts to labeled source data to promote the model training on target domain, facing the dilemmas caused by large domain shift and large camera variations. The non-overlapping labels challenge that source domain and target domain have entirely different persons further increases the re-identification difficulty. In this paper, we propose a novel algorithm to narrow such domain gaps. We derive a camera style adaptation framework to learn the style-based mappings between different camera views, from the target domain to the source domain, and then we can transfer the identity-based distribution from the source domain to the target domain on the camera level. To overcome the non-overlapping labels challenge and guide the person re-identification model to narrow the gap further, an efficient and effective soft-labeling method is proposed to mine the intrinsic local structure of the target domain through building the connection between GAN-translated source domain and the target domain. Experiment results conducted on real benchmark datasets indicate that our method gets state-of-the-art results.
. Image-to-image translation aims to translate an image to another one with given attributes changed. Recent literature based on GANs @cite_12 has shown impressive results in image-to-image translation. Typically, GANs consist of a generator @math and a discriminator @math , aiming to learn the true data distribution by a min-max game. Let @math be an image (usually in the tensor form with three channels) sampled from the given dataset, and @math be a random vector which obeys Gaussian or other distribution. @math and @math are corresponding probability distribution functions. The generator @math tries to generate fake images, such as @math , to fool the discriminator @math , while the discriminator tries to classify the real images and the fake images. It is essentially a generative framework in which the discriminator @math is introduced to find against with the generator @math a min-max game as below. Because @math and @math have the same optimization direction, the latter is often used for the sake of stability. Blurry images will not be tolerated since they look obviously fake, thus finally the generator can learn data distribution of real images and generated images that look exactly like the real ones.
@cite_12
2099471712
We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to ½ everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples.