paper review: Explaining and Harnessing Adversarial Examples (FGSM adversarial attack)

paper link: https://arxiv.org/abs/1412.6572 This paper introduces Fast Gradient Signed Method(FGSM) adversarial attack along with some useful insights on why linearity of deep learning networks would allow such attacks. The FGSM method is regarded as the method introduced after using L-BGFS method to generate adversarial samples. These two share similar ideas Read more…

How to implement ctc loss using tensorflow keras (feat. CRNN example)

Code: using tensorflow 1.14 The tk.keras.backend.ctc_batch_cost uses tensorflow.python.ops.ctc_ops.ctc_loss functions which has preprocess_collapse_repeated parameter. In some threads, it comments that this parameters should be set to True when the tf.keras.backend.ctc_batch_cost function does not seem to work, such as inconverging loss. However, my experience is that although setting this parameter to True Read more…

DenseNet paper review

paper url: https://arxiv.org/pdf/1608.06993.pdf Core Idea The core of DenseNet is using Dense blocks which is an essential of the idea behind it all. The core idea is that within a block, it contains multiple layers. All previous attempts before this paper only used the layers in sequential manner. An output Read more…

tensorRT stuff

tensorRT support matrix: https://docs.nvidia.com/deeplearning/dgx/integrate-tf-trt/index.html#matrix to apply the tensorRT optimizations, it needs to call create_inference_graph function. Check here for more details on this function. the graph that is fed to create_inference_graph should be freezed. To know more on what exactly means by “freezing”, check here. for using bare tensorRT python module, Read more…