Venue: ICLR 2020 Conference

Reviews:

final_score: rating: 6: Weak Accept
review_content: The paper makes a significant attempt at solving one of the practical problems in machine learning -- learning from many noisy and limited number of clean labels. This setting is presumably more practical than the setting of few-shot learning. Noisy labels are often abundantly available and investing in methods that can take the noise into account for building a discriminative model is quite timely. 

To be honest, the theoretical contribution of the paper is limited.  The authors make use of the nearest neighbour graph obtained from a reduced-dimensional set of features to compute the weights of the noisy labels that must guide the predictive model. From this perspective, the paper seems like an application of existing tools (such as CNN, graph convolutional network and binary classification). However, that does not undermine the superior results the authors have received in the novel application they have targeted. I appreciate the effort that went validating these ideas with real-world datasets.

In future, I would like to see a joint approach to such training, where the function g(), the nearest neighbour graph loss and the classification loss are all tied in the same objective function and are optimized jointly. 

The paper has few really minor grammatical errors and typos. Please fix those before uploading the final draft. 
review_title: Official Blind Review #2

final_score: rating: 6: Weak Accept
review_content: This paper presents a classification method when the data consists of few clean labels and many noisy labels. The authors propose to construct a graph structure within each class and use graph convolutional network to determine the clean/noisy labels of samples in each class. The model is based on a binary cross entropy loss function in each class, which learns the probability of labels to be clean. And such "clean" probability is used as the measure of relevance score between the sample different classes.

The idea of this paper is straightforward and the experimental results seem promising. The authors compare with several related methods and show the proposed method has better performance in few shot learning experiments.

For the motivation of this methods, why would the graph be constructed within each class? If there is correlation between different classes, how could the model use such class-wise correlation to clean the label?

Maybe I missed it, but how is the relevance score / predicted label determined for testing data given the graphs constructed in each class of training data?
review_title: Official Blind Review #1

final_score: rating: 6: Weak Accept
review_content: This paper studies the problem of learning from multiple tasks and additional noisy data. The proposed representation learning method first assigns each noisy data a relevance score using the topological information. Then the authors propose to minimize a combination of the loss of a class-prototype learning loss and a cosine classifier learning loss to learn a good representation generator g_theta. The empirical study validates the effectiveness of the proposed method.

I have the following comments,

1. The studied problem that learning from few-shot data and large-scale noisy data is interesting. According to the experimental results, the proposed method seems to be promising.

2. The learning procedure is confusing. It is highly recommended to provide the pseudocode of the proposed method.

3. Since there are many tasks and each task has a large-scale data, I'm afraid that the running time will explode. How to deal with this issue?
review_title: Official Blind Review #3

Meta-review: The paper combines graph convolutional networks with noisy label learning. The reviewers feel that novelty in the work is limited and there is a need for further experiments and  extensions. 
Decision: Reject