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View Code? Open in Web Editor NEW[TIP 2023] Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detection
License: MIT License
[TIP 2023] Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detection
License: MIT License
您好,根據paperwithcode,
您的論文是目前MVtec benchmark上面,
不使用額外訓練資料以及預訓練網路的條件下,
結果最佳的方法,
想請問為什麼只放在arxiv上而不投稿其他conference或journal呢
在get_current_images():方法中,返回值为四个,但在def train_one_epoch(): 接收值为三个
def get_current_images(self): """ Returns current images. Returns: [reals, fakes, fixed] """ reals = self.input_lap.data + self.input_res.data fakes = self.fake.data fake_lap = self.fake_lap.data fake_res = self.fake_res.data return **reals, fakes, fake_lap, fake_res**
def train_one_epoch(self): """ Train the model for one epoch. """ self.netg.train() epoch_iter = 0 for data in tqdm(self.data.train, leave=False, total=len(self.data.train)): self.total_steps += self.opt.batchsize epoch_iter += self.opt.batchsize self.set_input(data) self.optimize_params() if self.total_steps % self.opt.print_freq == 0: errors = self.get_errors() if self.opt.display: counter_ratio = float(epoch_iter) / len(self.data.train.dataset) self.visualizer.plot_current_errors(self.epoch, counter_ratio, errors) if self.total_steps % self.opt.save_image_freq == 0: **reals, fakes, fixed** = self.get_current_images()
ocr_gan_aug.py的test中,self.gt_labels设置为异常为0,正常为1。但是异常分数是越高越异常,这样算出来的auc是错误的吧?
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