diff --git a/onnxruntime/core/providers/cpu/math/sign.cc b/onnxruntime/core/providers/cpu/math/sign.cc
index abc3bdee16..60080135bb 100644
--- a/onnxruntime/core/providers/cpu/math/sign.cc
+++ b/onnxruntime/core/providers/cpu/math/sign.cc
@@ -58,12 +58,13 @@ struct CallSignImpl {
 template <>
 struct CallSignImpl<MLFloat16> {
   void operator()(const Tensor* input, Tensor* output) const {
-    ConstEigenVectorMap<Eigen::half> input_data(
-        reinterpret_cast<const Eigen::half*>(input->Data<MLFloat16>()),
-        narrow<ptrdiff_t>(input->Shape().Size()));
-
-    EigenVectorMap<Eigen::half>(reinterpret_cast<Eigen::half*>(output->MutableData<MLFloat16>()),
-                                narrow<ptrdiff_t>(output->Shape().Size())) = input_data.array().cwiseSign();
+    auto span = input->DataAsSpan<MLFloat16>();
+    auto output_data = output->MutableData<MLFloat16>();
+    std::transform(span.begin(), span.end(), output_data, [](const MLFloat16& val) {
+      // Return 0 as TF does for NaN.
+      if (val.IsNaNOrZero()) return MLFloat16::Zero;
+      return (val.IsNegative()) ? MLFloat16::MinusOne : MLFloat16::One;
+    });
   }
 };
 
diff --git a/onnxruntime/core/providers/cpu/nn/shrink.cc b/onnxruntime/core/providers/cpu/nn/shrink.cc
index bbfbb0eb11..406e8870b2 100644
--- a/onnxruntime/core/providers/cpu/nn/shrink.cc
+++ b/onnxruntime/core/providers/cpu/nn/shrink.cc
@@ -34,51 +34,25 @@ ONNX_CPU_OPERATOR_KERNEL(
 #endif
 namespace shrink_internal {
 template <class T>
-inline T ShrinkCore(const T& val, float bias, float lambd) {
+inline T ShrinkCore(const T& t_val, float bias, float lambd) {
   // The ONNX spec doesn't take numeric overflow and underflow into account
   // Implementing the spec as is for now
+  float val = static_cast<float>(t_val);
   if (val < -lambd) {
-    return T(val + bias);
+    return static_cast<T>(val + bias);
   }
   if (val > lambd) {
-    return T(val - bias);
+    return static_cast<T>(val - bias);
   } else {
-    return T(0);
+    return static_cast<T>(0.f);
   }
 }
 
-template <class T>
-Status ShrinkImpl(const Tensor* input, Tensor* output, float bias, float lambd) {
-  EigenMap<T>(*output) = EigenMap<T>(*input).unaryExpr([bias, lambd](const T& val) { return ShrinkCore<T>(val, bias, lambd); });
-  return Status::OK();
-}
-
-template <>
-Status ShrinkImpl<MLFloat16>(const Tensor* input, Tensor* output, float bias, float lambd) {
-  const auto span = input->DataAsSpan<MLFloat16>();
-  auto* output_data = output->MutableData<MLFloat16>();
-  std::transform(span.begin(), span.end(), output_data, [bias, lambd](const MLFloat16& val) {
-    float fl = val.ToFloat();
-    return MLFloat16(ShrinkCore<float>(fl, bias, lambd));
-  });
-  return Status::OK();
-}
-
-template <>
-Status ShrinkImpl<BFloat16>(const Tensor* input, Tensor* output, float bias, float lambd) {
-  const auto span = input->DataAsSpan<BFloat16>();
-  auto* output_data = output->MutableData<BFloat16>();
-  std::transform(span.begin(), span.end(), output_data, [bias, lambd](const BFloat16& val) {
-    float fl = val.ToFloat();
-    return BFloat16(ShrinkCore<float>(fl, bias, lambd));
-  });
-  return Status::OK();
-}
-
 template <class T>
 struct CallShrinkImpl {
   Status operator()(const Tensor* input, Tensor* output, float bias, float lambd) const {
-    return ShrinkImpl<T>(input, output, bias, lambd);
+    EigenMap<T>(*output) = EigenMap<T>(*input).unaryExpr([bias, lambd](const T& val) { return ShrinkCore<T>(val, bias, lambd); });
+    return Status::OK();
   }
 };