Revisiting Discriminative vs. Generative Classifiers: Theory and Implications

A large-scale deep model pre-trained on massive labeled or unlabeled data transfers well to downstream tasks. Linear evaluation freezes parameters in the pre-trained model and trains a linear classifier separately, which is efficient and attractive for transfer. However, little work has investigated the classifier in linear evaluation except for the default logistic regression. Inspired by the statistical efficiency of naive Bayes, the paper revisits the classical topic on discriminative vs. generative classifiers. Theoretically, the paper considers the surrogate loss instead of the zero-one loss in analyses and generalizes the classical results from binary cases to multiclass ones. We show that, under mild assumptions, multiclass naive Bayes requires $O(\log n)$ samples to approach its asymptotic error while the corresponding multiclass logistic regression requires $O(n)$ samples, where $n$ is the feature dimension. To establish it, we present a multiclass $\mathcal{H}$-consistency bound framework and an explicit bound for logistic loss, which are of independent interests. Simulation results on a mixture of Gaussian validate our theoretical findings. Experiments on various pre-trained deep vision models show that naive Bayes consistently converges faster as the number of data increases. Besides, naive Bayes shows promise in few-shot cases and we observe the "two regimes" phenomenon in pre-trained supervised models. Our code is available at https://github.com/ML-GSAI/Revisiting-Dis-vs-Gen-Classifiers.Comment: Accepted by ICML 2023, 58 page

Expression and biological significance of c-FLIP in human hepatocellular carcinomas

<p>Abstract</p> <p>Background</p> <p>c-FLIP can be considered as a tumor-progression factor in regard to its anti-apoptotic functions. In the present study, we intended to investigate the expression of c-FLIP in human HCC tissues, and its relation with drug-induced cell apoptosis through the specific inhibition of c-FLIP expression by siRNA in 7721 cells.</p> <p>Methods</p> <p>c-FLIP expression was quantified immunohistochemically in HCC tissues(eighty-six cases), and corresponding noncancerous tissues (fifty-seven cases). Patients with HCC were followed up for cancer recurrence. Then, the c-FLIP gene was silenced with specific siRNA in 7721 HCC cells. c-FLIP expression was detected by RT-PCR, Western Blot and immunocytochemical staining. The cellular viability and cell apoptosis were assayed <it>in vitro </it>with cells treated with doxorubicin.</p> <p>Results</p> <p>Positive immunostaining was detected for c-FLIP in 83.72% (72/86) human HCC tissues, 14.81% (4/27) hepatic cirrhosis, 11.11% (2/18) hepatic hemangioma tissues, and absent in normal hepatic tissues. The overexpression(more than 50%) of c-FLIP in HCC adversely affected the recurrence-free survival. Through c-FLIP gene silencing with siRNA, the expressions of c-FLIP mRNA and protein were remarkably down-regulated in 7721 HCC cells. And doxorubicin showed apparent inhibition on cell proliferations, and induced more apoptosis.</p> <p>Conclusion</p> <p>These results indicate that c-FLIP is frequently expressed in human HCCs, and its overexpression implied a lesser probability of recurrence-free survival. The specific silencing of c-FLIP gene can apparently up-regulate drug-induced HCC cell apoptosis, and may have therapeutic potential for the treatment of human HCC.</p

Mechanisms of Stress Tolerance in Xerophyte \u3cem\u3eZygophyllum xanthoxylum\u3c/em\u3e and Their Application in Genetic Improvement of Legume Forages

Xerophytes, naturally growing in desert areas, have evolved multiple protective mechanisms to survive and grow well in harsh environments. Zygophyllum xanthoxylum, a succulent xerophyte with excellent adaptability to adverse arid environments and a fodder shrub with high palatability and nutrient value, colonizes arid areas in China and Mongolia. In this study, we found that Z. xanthoxylum grew better responding to salt condition with a typical feature for halophytes and became more tolerant to drought in the presence of moderate salinity (50 mM NaCl); 50 mM NaCl alleviated deleterious impacts of drought on the growth of Z. xanthoxylum by improving the relative water content, inducing a significant drop in leaf water potential and, concomitantly, increasing leaf turgor pressure and chlorophyll concentrations resulting in an enhancement of overall plant photosynthetic activity. Subsequently, co-expression of genes encoding the tonoplast Na+/H+ antiporter (ZxNHX) and H+-PPase (ZxVP1-1) which involve in leaf Na+ accumulation under stress condition by compartmentalizing Na+ into vacuoles in Z. xanthoxylum significantly improved both drought and salt tolerance in legume forages, Lotus corniculatus L. and Medicago sativa L

Geo6D: Geometric Constraints Learning for 6D Pose Estimation

Numerous 6D pose estimation methods have been proposed that employ end-to-end regression to directly estimate the target pose parameters. Since the visible features of objects are implicitly influenced by their poses, the network allows inferring the pose by analyzing the differences in features in the visible region. However, due to the unpredictable and unrestricted range of pose variations, the implicitly learned visible feature-pose constraints are insufficiently covered by the training samples, making the network vulnerable to unseen object poses. To tackle these challenges, we proposed a novel geometric constraints learning approach called Geo6D for direct regression 6D pose estimation methods. It introduces a pose transformation formula expressed in relative offset representation, which is leveraged as geometric constraints to reconstruct the input and output targets of the network. These reconstructed data enable the network to estimate the pose based on explicit geometric constraints and relative offset representation mitigates the issue of the pose distribution gap. Extensive experimental results show that when equipped with Geo6D, the direct 6D methods achieve state-of-the-art performance on multiple datasets and demonstrate significant effectiveness, even with only 10% amount of data