\begin{figure}
	\centering
	\setlength{\tabcolsep}{1pt}
	\begin{tabular}{cccc}
		\includegraphics[width=20mm]{fig/base_5} &
		\includegraphics[width=20mm]{fig/base_10} & 
		\includegraphics[width=20mm]{fig/base_20} & 
		\includegraphics[width=20mm]{fig/train_base_30} \\
		\includegraphics[width=20mm]{fig/mab_5} &
		\includegraphics[width=20mm]{fig/mab_10} & 
		\includegraphics[width=20mm]{fig/mab_20} & 
		\includegraphics[width=20mm]{fig/train_mab_30} \\
		(a) & (b) & (c) & (d) \\
	\end{tabular}
	\caption{\textbf{TSNE of features}. The top row shows base features after global pooling only whereas the bottom shows features after adaptation on \textit{mini}ImageNet w/ a $5$-way $5$-shot model. \textbf{(a)}, \textbf{(b)} and \textbf{(c)} are embeddings of 5, 10 and 20 classes in the test-set. Finally, \textbf{(d)} are embeddings of 30 random classes in the train-set.}
	\vspace{-3mm}
	\label{fig:feat}
\end{figure}