diff --git a/src/bijectors/coupling_layer.jl b/src/bijectors/coupling_layer.jl
index 5904d506..68400f02 100644
--- a/src/bijectors/coupling_layer.jl
+++ b/src/bijectors/coupling_layer.jl
@@ -12,7 +12,7 @@ Implements
 Note that `PartitionMask` is _not_ a `Bijector`. It is indeed a bijection, but
 does not follow the `Bijector` interface.
 
-Its main use is in `CouplingLayer` where we want to partition the input into 3 parts,
+Its main use is in `Coupling` where we want to partition the input into 3 parts,
 one part to transform, one part to map into the parameter-space of the transform applied
 to the first part, and the last part of the vector is not used for anything.
 
@@ -135,10 +135,10 @@ Partitions `x` into 3 disjoint subvectors.
 @inline partition(m::PartitionMask, x) = (transpose(m.A_1) * x, transpose(m.A_2) * x, transpose(m.A_3) * x)
 
 
-# CouplingLayer
+# Coupling
 
 """
-    CouplingLayer{B, M, F}(mask::M, θ::F)
+    Coupling{F, M}(θ::F, mask::M)
 
 Implements a coupling-layer as defined in [1].
 
@@ -150,7 +150,7 @@ PartitionMask{SparseArrays.SparseMatrixCSC{Float64,Int64}}(
   [2, 1]  =  1.0, 
   [3, 1]  =  1.0)
 
-julia> cl = CouplingLayer(Shift, m, identity) # <= will do `y[1:1] = x[1:1] + x[2:2]`;
+julia> cl = Coupling(Shift, m, identity) # <= will do `y[1:1] = x[1:1] + x[2:2]`;
 
 julia> x = [1., 2., 3.];
 
@@ -176,75 +176,73 @@ Shift{Array{Float64,1},1}([2.0])
 # References
 [1] Kobyzev, I., Prince, S., & Brubaker, M. A., Normalizing flows: introduction and ideas, CoRR, (),  (2019). 
 """
-struct CouplingLayer{B, M, F} <: Bijector{1} where {B, M <: PartitionMask, F}
-    mask::M
+struct Coupling{F, M} <: Bijector{1} where {F, M <: PartitionMask}
     θ::F
+    mask::M
 end
 
-CouplingLayer(B, mask::M) where {M} = CouplingLayer{B, M, typeof(identity)}(mask, identity)
-CouplingLayer(B, mask::M, θ::F) where {M, F} = CouplingLayer{B, M, F}(mask, θ)
-function CouplingLayer(B, θ, n::Int)
+function Coupling(θ, n::Int)
     idx = Int(floor(n / 2))
-    return CouplingLayer(B, PartitionMask(n, 1:idx), θ)
+    return Coupling(θ, PartitionMask(n, 1:idx))
 end
 
-function CouplingLayer(cl::CouplingLayer{B}, mask::PartitionMask) where {B}
-    return CouplingLayer(B, mask, cl.θ)
+function Coupling(cl::Coupling{B}, mask::PartitionMask) where {B}
+    return Coupling(cl.θ, mask)
 end
 
 "Returns the constructor of the coupling law."
-coupling(cl::CouplingLayer{B}) where {B} = B
+coupling(cl::Coupling) = cl.θ
 
 "Returns the coupling law constructed from `x`."
-function couple(cl::CouplingLayer{B}, x::AbstractVector) where {B}
+function couple(cl::Coupling, x::AbstractVector)
     # partition vector using `cl.mask::PartitionMask`
     x_1, x_2, x_3 = partition(cl.mask, x)
 
     # construct bijector `B` using θ(x₂)
-    b = B(cl.θ(x_2))
+    b = cl.θ(x_2)
 
     return b
 end
 
-function (cl::CouplingLayer{B})(x::AbstractVector) where {B}
+function (cl::Coupling)(x::AbstractVector)
     # partition vector using `cl.mask::PartitionMask`
     x_1, x_2, x_3 = partition(cl.mask, x)
 
     # construct bijector `B` using θ(x₂)
-    b = B(cl.θ(x_2))
+    b = cl.θ(x_2)
 
     # recombine the vector again using the `PartitionMask`
     return combine(cl.mask, b(x_1), x_2, x_3)
 end
-function (cl::CouplingLayer{B})(x::AbstractMatrix) where {B}
+function (cl::Coupling)(x::AbstractMatrix)
     return hcat([cl(x[:, i]) for i = 1:size(x, 2)]...)
 end
 
 
-function (icl::Inverse{<:CouplingLayer{B}})(y::AbstractVector) where {B}
+function (icl::Inverse{<:Coupling})(y::AbstractVector)
     cl = icl.orig
     
     y_1, y_2, y_3 = partition(cl.mask, y)
 
-    b = B(cl.θ(y_2))
+    b = cl.θ(y_2)
     ib = inv(b)
 
     return combine(cl.mask, ib(y_1), y_2, y_3)
 end
-function (icl::Inverse{<:CouplingLayer{B}})(y::AbstractMatrix) where {B}
+function (icl::Inverse{<:Coupling})(y::AbstractMatrix)
     return hcat([icl(y[:, i]) for i = 1:size(y, 2)]...)
 end
 
-function logabsdetjac(cl::CouplingLayer{B}, x::AbstractVector) where {B}
+function logabsdetjac(cl::Coupling, x::AbstractVector)
     x_1, x_2, x_3 = partition(cl.mask, x)
-    b = B(cl.θ(x_2))
+    b = cl.θ(x_2)
 
     # `B` might be 0-dim in which case it will treat `x_1` as a batch
     # therefore we sum to ensure such a thing does not happen
     return sum(logabsdetjac(b, x_1))
 end
 
-function logabsdetjac(cl::CouplingLayer{B}, x::AbstractMatrix) where {B}
+function logabsdetjac(cl::Coupling, x::AbstractMatrix)
     r = [logabsdetjac(cl, x[:, i]) for i = 1:size(x, 2)]
 
     # FIXME: this really needs to be handled in a better way
diff --git a/test/bijectors/couplings.jl b/test/bijectors/couplings.jl
index 19ca7fe4..49356c26 100644
--- a/test/bijectors/couplings.jl
+++ b/test/bijectors/couplings.jl
@@ -7,7 +7,7 @@ using Tracker
 import Flux
 
 using Bijectors:
-    CouplingLayer,
+    Coupling,
     PartitionMask,
     coupling,
     couple,
@@ -15,7 +15,7 @@ using Bijectors:
     combine,
     Shift
 
-@testset "CouplingLayer" begin
+@testset "Coupling" begin
     @testset "PartitionMask" begin
         m1 = PartitionMask(3, [1], [2])
         m2 = PartitionMask(3, [1], [2], [3])
@@ -32,12 +32,12 @@ using Bijectors:
 
     @testset "Basics" begin
         m = PartitionMask(3, [1], [2])
-        cl1 = CouplingLayer(Shift, m, x -> x[1])
+        cl1 = Coupling(x -> Shift(x[1]), m)
 
         x = [1., 2., 3.]
         @test cl1(x) == [3., 2., 3.]
 
-        cl2 = CouplingLayer(θ -> Shift(θ[1]), m, identity)
+        cl2 = Coupling(θ -> Shift(θ[1]), m)
         @test cl2(x) == cl1(x)
 
         # inversion
@@ -63,7 +63,7 @@ using Bijectors:
         m = PartitionMask(length(x), [1], [2])
         nn = Flux.Chain(Flux.Dense(1, 2, Flux.sigmoid), Flux.Dense(2, 1))
         nn_tracked = Flux.fmap(x -> (x isa AbstractArray) ? Tracker.param(x) : x, nn)
-        cl = CouplingLayer(Shift, m, nn_tracked)
+        cl = Coupling(θ -> Shift(nn_tracked(θ)), m)
 
         # should leave two last indices unchanged
         @test cl(x)[2:3] == x[2:3]