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Flamingo a Visual Language Model for Few-Shot Learning
Learning Transferable Visual Models From Natural Language Supervision
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What matters when building vision-language models?
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gates
#quantum-computing
NOT-Gate (X-Gate)
: $$\sigma_x = \begin{pmatrix} 0 & 1 \ 1 & 0 \end{pmatrix}$$
|
0
⟩
σ
x
=
|
1
⟩
and
|
1
⟩
σ
x
=
|
0
⟩
Phase Gate (NOT-Gate in the diagonal basis)
:$$\sigma_z = \begin{pmatrix}1 & 0 \ 0 & -1\end{pmatrix}$$
|
0
⟩
σ
z
=
|
0
⟩
and
|
1
⟩
σ
z
=
−
|
1
⟩
. This could also be generalised to: $$\begin{pmatrix}1 & 0 \ 0 & e^{i\phi}\end{pmatrix}$$ which sends
|
0
⟩
↦
|
0
⟩
and
|
1
⟩
↦
e
i
ϕ
|
1
⟩
**T-Gate(
θ
=
ϕ
/
4
): $$\begin{pmatrix}1 & 0 \ 0 & e^{i\pi/4}\end{pmatrix}$$
Hadamard Gate
: $$\frac{1}{\sqrt2}\begin{pmatrix} 1 & 1 \ 1 & -1\end{pmatrix}$$
|
0
⟩
↦
|
0
⟩
+
|
1
⟩
2
and
|
1
⟩
↦
|
0
⟩
−
|
1
⟩
2
Controlled-NOT Gate(CNOT Gate)
a
b
c
0
0
0
0
1
1
1
0
1
1
1
0
Toffoli Gate