Robust deep learning object recognition models rely on low frequency information in natural images.

Machine learning models have difficulty generalizing to data outside of the distribution they were trained on. In particular, vision models are usually vulnerable to adversarial attacks or common corruptions, to which the human visual system is robust. Recent studies have found that regularizing machine learning models to favor brain-like representations can improve model robustness, but it is unclear why. We hypothesize that the increased model robustness is partly due to the low spatial frequency preference inherited from the neural representation. We tested this simple hypothesis with several frequency-oriented analyses, including the design and use of hybrid images to probe model frequency sensitivity directly. We also examined many other publicly available robust models that were trained on adversarial images or with data augmentation, and found that all these robust models showed a greater preference to low spatial frequency information. We show that preprocessing by blurring can serve as a defense mechanism against both adversarial attacks and common corruptions, further confirming our hypothesis and demonstrating the utility of low spatial frequency information in robust object recognition.

Understanding Robustness and Generalization of Artificial Neural Networks Through Fourier Masks.

Despite the enormous success of artificial neural networks (ANNs) in many disciplines, the characterization of their computations and the origin of key properties such as generalization and robustness remain open questions. Recent literature suggests that robust networks with good generalization properties tend to be biased toward processing low frequencies in images. To explore the frequency bias hypothesis further, we develop an algorithm that allows us to learn modulatory masks highlighting the essential input frequencies needed for preserving a trained network's performance. We achieve this by imposing invariance in the loss with respect to such modulations in the input frequencies. We first use our method to test the low-frequency preference hypothesis of adversarially trained or data-augmented networks. Our results suggest that adversarially robust networks indeed exhibit a low-frequency bias but we find this bias is also dependent on directions in frequency space. However, this is not necessarily true for other types of data augmentation. Our results also indicate that the essential frequencies in question are effectively the ones used to achieve generalization in the first place. Surprisingly, images seen through these modulatory masks are not recognizable and resemble texture-like patterns.

Recurrent computations for visual pattern completion.

Making inferences from partial information constitutes a critical aspect of cognition. During visual perception, pattern completion enables recognition of poorly visible or occluded objects. We combined psychophysics, physiology, and computational models to test the hypothesis that pattern completion is implemented by recurrent computations and present three pieces of evidence that are consistent with this hypothesis. First, subjects robustly recognized objects even when they were rendered <15% visible, but recognition was largely impaired when processing was interrupted by backward masking. Second, invasive physiological responses along the human ventral cortex exhibited visually selective responses to partially visible objects that were delayed compared with whole objects, suggesting the need for additional computations. These physiological delays were correlated with the effects of backward masking. Third, state-of-the-art feed-forward computational architectures were not robust to partial visibility. However, recognition performance was recovered when the model was augmented with attractor-based recurrent connectivity. The recurrent model was able to predict which images of heavily occluded objects were easier or harder for humans to recognize, could capture the effect of introducing a backward mask on recognition behavior, and was consistent with the physiological delays along the human ventral visual stream. These results provide a strong argument of plausibility for the role of recurrent computations in making visual inferences from partial information.

Dietary Carbohydrate, Glycemic Index, Glycemic Load, and Breast Cancer Risk Among Mexican Women.

BACKGROUND: Very few studies have focused on the relationship among dietary carbohydrates, glycemic index (GI), glycemic load (GL), and breast cancer risk in Latin American women. Our objective was to assess the associations among dietary carbohydrate, GI, GL, and risk of breast cancer, and to further investigate these associations by levels of overweight/obesity and physical activity. METHODS: We used data from a Mexican population-based case-control study. We recruited a 1,000 women with incident breast cancer and 1,074 matched control women ages 35 to 69 years between 2004 and 2007. We used conditional logistic regression models and energy-adjusted carbohydrates, GI, and GL using the residual method. RESULTS: Total carbohydrate intake was associated with an increased risk of breast cancer among premenopausal women. The odds ratio in the highest versus the lowest quartile was 1.3 (95% confidence interval = 1.0, 1.7; P trend = 0.03). In stratified analyses by body mass index (BMI), the positive association between carbohydrate and risk of premenopausal breast cancer was only observed among overweight women. The odds ratio comparing the top with the bottom quartile was 1.9 (95% confidence interval = 1.2, 3.0; P trend = 0.01) among women with BMI ≥ 25 kg/m. No association was observed among women with BMI < 25 kg/m. CONCLUSIONS: Our findings suggest that high carbohydrate diets are associated with an increased risk of breast cancer among premenopausal Mexican women.

Anthropometry, silhouette trajectory, and risk of breast cancer in Mexican women.

BACKGROUND: Obesity has been associated with breast cancer risk in the Caucasian population but the association remains unclear in the Hispanics. Previous studies conducted among Hispanics in the U.S. have shown inconsistent results. PURPOSE: The association between anthropometry, body shape evolution across lifetime, and the risk of breast cancer was assessed using a multi-center population-based case-control study conducted in Mexico. METHODS: One thousand incident cases and 1074 matched control women aged 35-69 years were recruited between 2004 and 2007, and analyzed in 2011-2012. Conditional logistic regression models were used. RESULTS: Height was related to an increased risk of breast cancer in both premenopausal (p trend=0.03) and postmenopausal women (p trend=0.002). In premenopausal women, increase in BMI; waist circumference (WC); hip circumference (HC); and waist-hip ratio (WHR) were inversely associated with breast cancer risk (p trends<0.001 for BMI and WC, 0.003 for HC, and 0.016 for WHR). In postmenopausal women, decreased risks were observed for increased WC (p trend=0.004) and HC (p trend=0.009) among women with time since menopause <10 years. Further analysis of body shape evolution throughout life showed strong and significant increase in risk of breast cancer among women with increasing silhouettes size over time compared to women with no or limited increase. CONCLUSIONS: These findings suggest that anthropometric factors may have different associations with breast cancer risk in Hispanic women than in Caucasian women. This study also shows the importance of considering the evolution of body shape throughout life.