A Semi-Supervised Two-Stage Approach to Learning from Noisy Labels

The recent success of deep neural networks is powered in part by large-scale well-labeled training data. However, it is a daunting task to laboriously annotate an ImageNet-like dateset. On the contrary, it is fairly convenient, fast, and cheap to collect training images from the Web along with their noisy labels. This signifies the need of alternative approaches to training deep neural networks using such noisy labels. Existing methods tackling this problem either try to identify and correct the wrong labels or reweigh the data terms in the loss function according to the inferred noisy rates. Both strategies inevitably incur errors for some of the data points. In this paper, we contend that it is actually better to ignore the labels of some of the data points than to keep them if the labels are incorrect, especially when the noisy rate is high. After all, the wrong labels could mislead a neural network to a bad local optimum. We suggest a two-stage framework for the learning from noisy labels. In the first stage, we identify a small portion of images from the noisy training set of which the labels are correct with a high probability. The noisy labels of the other images are ignored. In the second stage, we train a deep neural network in a semi-supervised manner. This framework effectively takes advantage of the whole training set and yet only a portion of its labels that are most likely correct. Experiments on three datasets verify the effectiveness of our approach especially when the noisy rate is high

Implementation of Objects Recognition in Seismic Image via Artificial Neural Network (ANN)

Seismic image processing is necessary in oil and gas exploration to identify the existence of potential reservoir by classifying the seismic image into different sections. These sections, also known as objects made up of different patterns which portraying the structure of subsurface. This project aims to develop a data mining algorithm embedded in a system that has ability to recognize the objects of channel and fault in seismic image. The method chosen is artificial neural network (ANN) which consists of input layer, hidden layer and output layer. Each layer is made up of numbers of neuron nodes to receive input data from preceding layers and output value to next layer until final output is determined from output layer. The ANN is trained and tested via MATLAB Neural Network Pattern Recognition Toolbox (nprtool) and MATLAB Neural Network Toolbox (nntool). 2-dimension (2D) seismic image is converted into gray scale image via MATLAB Image Processing Toolbox (imtool) and Grey-level co-occurrence matrix (GLCM) which serve as input to the ANN is retrieved from the gray scale image. Result is displayed by the system informing user whether the input image is channel, fault or neither both