Reversed Naive Bayes - likelihood and parameter estimation
To elaborate on my comment, the first image depicts conditional dependence of $X_i | Y$ for $i \in 1,...,m$. 
Therefore, your likelihood for this model would be  

$$
\begin{aligned}
P(Y = y) \times P(X_1 | Y = y) \times ... \times P(X_m | Y = y)
\end{aligned}
$$

The second image (bottom) depicts $Y | X_i$, therefore the likelihood would be

$$
P(X_1) \times P(X_2) \times ... \times P(Y | X_1, X_2, ..., X_m) \\
\ = P(X_1) \times P(X_2) \times ... \times P(Y | X_1) \times ... \times P(Y | X_m)
$$

Assuming that both scenarios do not have conditional independence, the two are not equivalent!