NLNL: Negative Learning for Noisy Labels (1908.07387v1)

Abstract: Convolutional Neural Networks (CNNs) provide excellent performance when used for image classification. The classical method of training CNNs is by labeling images in a supervised manner as in "input image belongs to this label" (Positive Learning; PL), which is a fast and accurate method if the labels are assigned correctly to all images. However, if inaccurate labels, or noisy labels, exist, training with PL will provide wrong information, thus severely degrading performance. To address this issue, we start with an indirect learning method called Negative Learning (NL), in which the CNNs are trained using a complementary label as in "input image does not belong to this complementary label." Because the chances of selecting a true label as a complementary label are low, NL decreases the risk of providing incorrect information. Furthermore, to improve convergence, we extend our method by adopting PL selectively, termed as Selective Negative Learning and Positive Learning (SelNLPL). PL is used selectively to train upon expected-to-be-clean data, whose choices become possible as NL progresses, thus resulting in superior performance of filtering out noisy data. With simple semi-supervised training technique, our method achieves state-of-the-art accuracy for noisy data classification, proving the superiority of SelNLPL's noisy data filtering ability.

• The paper demonstrates that Negative Learning reduces overfitting by using complementary label selection to mitigate the impact of noise.
• It introduces the SelNLPL framework that dynamically distinguishes clean data for effective Positive Learning during training.
• Empirical results on datasets like CIFAR-10 underscore substantial accuracy improvements under both symmetric and asymmetric noise conditions.

Analyzing "NLNL: Negative Learning for Noisy Labels"

The paper entitled "NLNL: Negative Learning for Noisy Labels" presents a novel approach to addressing the pervasive challenge of training convolutional neural networks (CNNs) in the presence of noisy labels. This is a pertinent issue as noisy labels can lead to significant performance degradation in image classification tasks, owing to the tendency of CNNs to overfit to incorrect information. The authors propose an indirect training method called Negative Learning (NL), alongside a novel framework known as Selective Negative Learning and Positive Learning (SelNLPL), which combines both negative and conventional positive learning techniques to mitigate the effects of label noise.

Core Concepts and Methodologies

The cornerstone of the proposed work is Negative Learning (NL), wherein CNNs are trained using a complementary label strategy—asserting that a given input image does not belong to a randomly selected complementary label. This is opposed to the traditional Positive Learning (PL) method, where the model is trained to align input images with designated labels. The authors contend that NL is less prone to providing incorrect information, as the probability of randomly selecting the true label as complementary is comparatively low.

To enhance training efficiency and optimize convergence, the authors employ SelNLPL, a framework integrating NL and selective PL. This framework selectively applies PL to data with a high likelihood of being clean, as assessed during the NL process. Such selective application narrows the confidence gap between clean and noisy data, facilitating effective filtering of noise during training. Notably, this approach does not rely on prior knowledge regarding the quantity or distribution of noise—offering practicality and robustness in real-world applications.

Numerical Results and Evaluation

The paper reports robust empirical performance on popular datasets such as CIFAR-10, CIFAR-100, and FashionMNIST. Through experiments with symmetric and asymmetric label noise, the proposed approach demonstrates superior accuracy in noisy data classification, achieving state-of-the-art results particularly when leveraging semi-supervised learning techniques like pseudo labeling. For example, on CIFAR10 with symmetric noise, SelNLPL consistently achieved higher accuracy when compared to various existing methods, showcasing the robustness of the proposed approach.

The precision-recall metrics further underline the high efficacy of their noise filtering strategy, indicating substantial improvements over traditional PL approaches. In situations of high noise levels, SelNLPL effectively estimates and filters out noise, which has been a significant challenge in previous approaches.

Implications and Future Directions

The proposed methods hold substantial implications for training robust CNNs under real-world conditions where label noise is inevitable. The use of NL provides a strategic shift from conventional learning paradigms, focusing on leveraging incorrect labels beneficially, thereby enhancing the model’s resistance to noise. Additionally, SelNLPL offers a refined training methodology that allows models to capitalize on cleaner data subsets dynamically identified during training, which optimizes the learning process.

This research paves the way for further exploration into maximizing complementary label selection strategies and refining the balance between positive and negative learning paradigms for different applications. Future investigations may delve into extending these approaches across other domains or integrating with novel architectures, such as transformers in vision tasks, to push the boundaries of noise-robust learning.

Overall, the diligent articulation of concepts and empirical validation within this paper highlights NLNL as a promising direction in the landscape of machine learning robustness and noise-averse model training, with broad implications for practical deployment in noisy environments.